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Dmitry Grinberg pub­lished a long piece ex­plain­ing his dis­taste for RISC-V, you can read his ar­ti­cle here: RISC-V: They Should Have Known Better - Dmitry.GR. It went to the front page of Hacker News and it started a good ar­gu­ment on Lobsters. It is the most sub­stan­tial crit­i­cism the ar­chi­tec­ture has had in a while and though I switched my en­tire stack away from STM32 and ARM to RISC-V and did a video on it about a year ago Good­bye STM32 ARM — Meet the CH32 RISC-V Chips That Replaced It! , part of me is in­fu­ri­ated be­cause so much of what he said seems like a bi­ased per­spec­tive.

Look, I am not go­ing to de­fend the ISA com­mit­tee, RISC-V in­ter­na­tional de­nied me mem­ber­ship to their golden tower. On the ar­chi­tec­ture it­self, the com­pressed store off­sets re­ally are strange, Zicsr re­ally should not be a sep­a­rate thing you have to re­mem­ber to ask for, I have hit every one of these and I have writ­ten a book thats about 80% com­plete about hit­ting them on the CH32V003, which is one of the very “RV32E” type chip he men­tions.

Maybe I should say where I am writ­ing from, be­cause it changes which parts of this ar­gu­ment look im­por­tant from my per­spec­tive.

I work out of Trinidad and Tobago, a small is­land na­tion off the coast of Venezuela. When I want a de­vel­op­ment board I am not click­ing through to next day de­liv­ery, I am check­ing whether the seller ships here at all, what cus­toms will do to it (if I get it at all), and what the to­tal lands at in TT dol­lars. “Free Shipping” from Digikey, Mouser or any US or European man­u­factuer dosen’t ap­ply to me. I pay any­where from US $60 to US $200 to ship one dol­lar chips that peo­ple every­where else get free ship­ping on. In fact a well known PCB com­pany who reached out to me con­sid­er­ing spon­sor­ship turned me down so­ley based on ship­ping to my lo­ca­tion. Have a look here:

The stu­dents I want to teach are in the same po­si­tion, and so are the ones in Nigeria and Bangladesh and every­where else the peo­ple in the in­dus­try does not think about when it writes its blog posts. From that po­si­tion, the dif­fer­ence be­tween a ten cent part and a one dol­lar part is not a round­ing er­ror and it is not a de­tail you get to wave past on the way to the in­ter­est­ing dis­cus­sion about en­cod­ings. It is the dif­fer­ence be­tween a class of thirty stu­dents each hav­ing their own chip and a class of thirty stu­dents watch­ing one demo board if any at all. Instruction set el­e­gance is a thing you can af­ford to care about once the hard­ware is al­ready on your desk. Whether the hard­ware can get to your desk at all comes first. That is why the para­graph most peo­ple scrolled past is, to me, the most im­por­tant one in the ar­ti­cle.

Grinberg missed that part that RISC-V cre­ates a space for the other 99% out­side of “the world” (which in this space “world” is mainly the US and Europe) and it has noth­ing to do with ar­chi­tec­ture.

He Derives the Requirements and Lands on RV32EC

Before the in­ter­rupt arith­metic, be­fore the en­cod­ing com­plaints, he does some­thing care­ful. He asks what a cheap mi­cro­con­troller core is ac­tu­ally for. His an­swer is that it sits in­side a larger chip prod­ding reg­is­ters and con­fig­ur­ing hard­ware blocks, in an “MP3 player, an SD card, a USB stick”. The real work is done by cus­tom sil­i­con around it. From that he de­rives what such a core needs. Low in­ter­rupt la­tency a small die area and good code den­sity, be­cause the code lives in ROM or SRAM and both are ex­pen­sive per byte. No hard­ware di­vider, pos­si­bly not even a mul­ti­plier, since you are not do­ing much arith­metic. No priv­i­lege sep­a­ra­tion, be­cause noth­ing un­trusted ever runs there.

Then he writes the line him­self:

“But,” you might say, “you just de­scribed RV32IC (or RV32EC)!”

“But,” you might say, “you just de­scribed RV32IC (or RV32EC)!”

And ear­lier, plainly:

I am 100% sure that RISC-V will own the cheap-as-dirt sin­gle-use mi­cro­con­troller space even­tu­ally.

I am 100% sure that RISC-V will own the cheap-as-dirt sin­gle-use mi­cro­con­troller space even­tu­ally.

So the most cred­i­ble RISC-V critic of the month sat down, worked out from first prin­ci­ples what a cheap mi­cro­con­troller core should be, ar­rived at the in­struc­tion set a ten cent chip im­ple­ments, and stated that this seg­ment is go­ing to be RISC-V’s.

He de­rives the case for the chip and then spends the rest of the ar­ti­cle an­noyed that the chip ex­ists.

This is al­most satir­i­cal.

His quar­rel is with whether that out­come was earned. That is a real ques­tion and I un­der­stand why it both­ers him. It is not, how­ever, a ques­tion that af­fects any­body de­cid­ing what to learn on, be­cause the chip is on the shelf ei­ther way.

Where I Actually Disagree, Strongly.

His cen­tral claim is the first one in the ar­ti­cle, and it is big­ger than any of the en­cod­ing com­plaints:

Simply put, the things a high-end CPU needs are di­a­met­ri­cally op­posed to the things a small cost-sav­ing mi­cro­con­troller core needs.

Simply put, the things a high-end CPU needs are di­a­met­ri­cally op­posed to the things a small cost-sav­ing mi­cro­con­troller core needs.

The con­clu­sion he draws is that no sin­gle ISA can serve both ends, and that RISC-V fans are fool­ing them­selves, in the­ory the premise is true. The con­clu­sion does not fol­low, and I can show you why from three parts sit­ting on my desk as we speak.

CH32V003. This is the cheap “RV32EC” with six­teen reg­is­ters, no mul­ti­plier, no di­vider, ma­chine mode only, 2KB of SRAM, 16KB of flash, ten cents, it’s EXACTLY the core he spec­i­fied. I shipped two prod­ucts with these, one is a bin mon­i­tor that has a ToF sen­sor, an LED and an air tag. The other is an agri­cul­tural prod­uct for a client that opens and closes a door at a cer­tain time. It also makes a good throw away part, as I show case in my whis­tle switch Clap Switch Is Dead. Here’s the RISC-V Powered Whistle Switch! and which in my view is the BEST part to re­place the over­priced, out­dated Arduino Did Arduino Q Ruin Arduino? - Here’s how to Switch to RISC-V with the CH32V003.

CH32H417. A dual core MCU that is un­matched in per­for­mance to price point and is at the higher end of the MCU line of things. It has a QingKe V5F at 400 MHz along­side a V3F at 144 MHz, 896KB of SRAM, 960KB of flash. USB 3.2 Gen1 with an in­te­grated 5 Gbps trans­ceiver, 100M Ethernet MAC and PHY, a SerDes iso­lated trans­ceiver, a 500 MB/s high speed in­ter­face, SDMMC, a cam­era in­ter­face, a dis­play con­troller, a graph­ics ac­cel­er­a­tor etc etc. I got a web browser run­ning on this thing I Built a Web Browser on a RISC-V Microcontroller (No Linux) Quantum en­tropy based GAN cat gen­er­a­tion Schrödinger’s De/Motivational Quantum Cat: GAN Image Generation on CH32 RISC-V Microcontroller and real-time fa­cial recog­ni­tion Real Time Facial Recognition on The Edge With CH32H417 RISC-V MCU in un­der 150KB of ram. I got a host of other pro­jects run­ning but those are just some I got time to record and put up.

Baochip. A VexRISC-V with an MMU built around a stack thats open from sil­i­con to os Baochip-1x: A Mostly-Open, 22nm SoC for High Assurance Applications « bun­nie’s blog, that runs Xous be­trusted-io/​xous-core: The Xous mi­cro­ker­nel de­signed by leg­endary hard­ware hacker “bunnie” Huang , a Rust mi­cro­ker­nel with real process iso­la­tion. Privilege sep­a­ra­tion, the ex­act thing he says the cheap end does not need and there­fore does not get. In ad­di­tion to Xous it also sup­ports op­er­at­ing sys­tems like SEL4 vk2seb/bao1x-seL4: seL4 port to baochip-1x and Linux pkoscik/​baochip-linux: An at­tempt to boot main­line Linux on a stock Dabao board. I wrote the bare metal C SDK for the chip Arm­strong­Subero/​dabao-sdk: Bare metal C SDK for the Baochip-1x RISC-V SoC and it was of course the chip in­side the badge of DEFCON 34 The New Defcon Badges Pack a Unique Open Source Chip That Doubles as a Security Key | WIRED this year.

I can also point to the NES em­u­la­tor I wrote for the $1 ESP32C3 RISC-V based chip NES Emulator on $1 ESP32-C3 RISC-V Microcontroller, or ex­per­i­ment­ing with Linux on the Orange Pi RV2 OrangePi RV2 5 Minute Unboxing and Setup | RISC-V Ubuntu Linux that takes 5 min­utes to setup and has been run­ning since the day I boot it up.

Point is I could go on and on about how di­verse and ac­ces­si­ble cur­rently ship­ping RISC-V parts are, but then we’ll be stray­ing too much from the topic at hand.

I linked all those to say this, that all these parts all have the same base in­struc­tion set and I gained ex­per­tise in all in un­der a year and un­der US $100 across the en­tire stack, from dis­posi­ble sil­i­con to PC level, of course mi­nus data cen­ter com­pute.

For un­der US $100 in­clud­ing ship­ping I was able to ex­plore an en­tire ver­ti­cal stack us­ing one ar­chi­tec­ture. Due to the AI race the OrangePi RV2 has now gone up in price but at re­lease it cost $30 and shipped free. For about 7 dol­lars I got 50 CH32V003s with a de­bug­ger, the CH32H417 board is $20 on ana­log lamb and uses the same cheap (and of­fi­cial) de­bug­ger for the CH32V003 and the Baochip Dabao board (which I wrote a book about by the way check it out here (The Dabao Book - Payhip) was $9.50 on crowd sup­ply when I bought it, two with ship­ping from crowd sup­ply cost me $35, un­der $100 in to­tal. A de­bug­ger for an ARM part alone a Segger J-Link costs about $600, though I guess for that $100, and add an­other $100 to ship,so about $200 I could get an EDU edi­tion J-link and no chips or boards. Yaay.

Back to RISC-V, across all these parts, the base set is the same. So that means the same reg­is­ter model, same call­ing con­ven­tion, same tool­chain. Yes the ex­ten­sions dif­fer, but the thing is what I learned writ­ing as­sem­bly on the ten cent CH32V003 part did not stop be­ing true on any of the oth­ers. A dual core MCU, an SBC run­ning Linux or an ad­vanced cus­tom se­cu­rity chip run­ning a novel op­er­at­ing sys­tem. My skills were trans­ferrable to the point that in each case within a few hours I had tool­chains setup, could fo­cus on my ap­pli­ca­tions and when de­bug­ging I felt at home. All I need to work with them is the ISA man­ual and a C com­piler.

Now price the same jour­ney on the other side, for­get x86 – 64 and that du­op­oly, patent mine­field, with multi-thou­sand dol­lar de­bug probes; we’ll take a look at ARM.

The equiv­a­lent to the CH32V003 is the Cortex-M0 is ARMv6-M so some­thing like an STM32F030, step it up we have a Cortex-M7 which is ARMv7-M, to get an MMU in a part for Linux or SEL4 and Xous, you’re look­ing at an ap­pli­ca­tion proces­sor like the ARMv8-A.  These are dif­fer­ent Arm pro­files with sig­nif­i­cantly dif­fer­ent priv­i­lege, ex­cep­tion, and sys­tem mod­els, so mov­ing up the stack in­volves sub­stan­tially more re­learn­ing than sim­ply en­abling an­other RISC-V ex­ten­sion. Trust me I’ve used them all.

And at the top of that range the gap is not even about learn­ing curves. There is no Cortex-M mi­cro­con­troller with an in­te­grated USB 3.0 SuperSpeed PHY. The near­est dual core Arm part is an STM32H747, which is a fine chip and does not have one. If you need USB 3.0 you leave the mi­cro­con­troller class en­tirely: an i.MX 8 or an RK3xxx, which means Cortex-A. You want an MMU, Linux, DDR, a PMIC, and a board you are not lay­ing out in two lay­ers. Or you keep the M7 and add an ex­ter­nal bridge chip.

The H417 eval­u­a­tion board is around twenty dol­lars. The H747 in TFBGA240 car­ries a twenty week man­u­fac­turer lead time, chip only, costs about the same, be­fore you have any­thing to plug in, and Mouser asks for ID be­fore you can or­der, Digikey has also been known to deny peo­ple parts de­pend­ing on where they are and their name as Hussein Ali, well known Youtuber from NorthridgeFix de­scribes Star­link Repair - Digi-key re­fused my or­der.. Oh and it’s about US $60 – 100+ to ship to my lo­ca­tion. I can pick up H417s on the of­fi­cial WCH store on Aliexpress with free ship­ping and no ver­i­fi­ca­tion hul­la­balu. We haven’t even started talk­ing about the Cortex-A parts that have MMUs or thier de­bug­ging tools and ecosys­tem frag­men­ta­tion.

The Boundary Is Not Technical

Here is the part that un­der­cuts his fram­ing most di­rectly, and it has noth­ing to do with en­cod­ings. He treats the gap be­tween a small core and a large one as an ar­chi­tec­tural fact, some­thing that falls out of op­posed re­quire­ments. On ARM chips it is not an ar­chi­tec­tural fact. It is a PRODUCT bound­ary, and it is en­forced by li­cens­ing. Has any­one tried adding an MMU to a Cortex-M? The phys­i­cal trade­offs are real, the dif­fer­ence is that with RISC-V, the ISA owner does not de­cide for you where that bound­ary must be drawn. If you want vir­tual mem­ory on ARM you li­cense a Cortex-A in­stead, which is a dif­fer­ent core fam­ily, a dif­fer­ent pro­file, a dif­fer­ent ne­go­ti­a­tion, and a dif­fer­ent roy­alty. There is no in­cre­men­tal path. there is a wall, with a sales team on the other side of it.

Compare what hap­pened with Baochip. The RISC-V priv­i­leged spec­i­fi­ca­tion de­fines su­per­vi­sor mode and Sv32 pag­ing as op­tional things an im­ple­men­ta­tion may pro­vide. VexRISC-V is an open core, some­body added an MMU to it. bun­nie built a chip around it and runs a mi­cro­ker­nel with real process iso­la­tion on it that me in Trinidad a coun­try who’s name does not even come up in ISA cir­cles can ex­per­i­ment with at low cost and teach to other peo­ple in the re­gion.

That’s what free­dom looks like.

Nobody asked per­mis­sion, no­body signed any­thing, no­body pays a roy­alty per unit shipped and any­body can learn down to the RTL the sil­i­con is built on.  So when Grinberg in his ar­ti­cle lists priv­i­lege sep­a­ra­tion among the things the cheap end does not need and there­fore does not get, it is de­scrib­ing a prop­erty of ARM’s prod­uct seg­men­ta­tion and at­tribut­ing it to in­struc­tion set de­sign. On RISC-V it is a check­box in the priv­i­leged spec, you leave it off in a ten cent part be­cause it costs area you do not want to spend, and you turn it on when you do, and the in­struc­tion set un­der­neath is the same ei­ther way.

That is the real dif­fer­ence be­tween the two ecosys­tems, and it is why “one ISA can­not serve both ends” reads dif­fer­ently de­pend­ing on which side you are stand­ing on. On one side the ends are sep­a­rated by physics and cost, on the other they are sep­a­rated by physics, cost, and a con­tract.

The Thing He Calls Fragmentation

Before I close I want to ad­dress his stance on frag­men­ta­tion. He is not wrong that the ex­ten­sion mech­a­nism frag­ments the stan­dard. Zcb split­ting off from C is an­noy­ing and Zicsr not be­ing im­plied by the base is an­noy­ing. Vendors adding pro­pri­etary in­ter­rupt hard­ware does frag­ment things fur­ther, I learned first hand port­ing NuttX to the CH32V307 Porting Apache NuttX RTOS to the WCH CH32V307: A Deep Dive into the PFIC and Everything That Went Wrong.

But that mech­a­nism is the an­swer to his own open­ing ques­tion. The rea­son one in­struc­tion set can sit in a ten cent part with six­teen reg­is­ters and also in a chip run­ning a pro­tected multi-process op­er­at­ing sys­tem is pre­cisely that the small part is not car­ry­ing the large part’s bag­gage. There is no com­pro­mise core in the mid­dle serv­ing both badly, which is what “diametrically op­posed re­quire­ments” would nor­mally force. Fragmentation and scal­a­bil­ity are the same prop­erty, you do not get one with­out the other and whether the trade­off was worth it is a fair ar­gu­ment and I do not think it has an ob­vi­ous an­swer.

What I do think is that he is right about the im­por­tant part, and right in a way that favours the thing he is crit­i­cis­ing. RISC-V is not go­ing to take the cheap mi­cro­con­troller space be­cause its en­cod­ing is el­e­gant. It is go­ing to take it be­cause the part costs ten cents, and be­cause the lad­der above it is the same in­struc­tion set all the way up. It is go­ing there be­cause an em­bed­ded en­gi­neer in a 3rd world coun­try can shine a cheap LED and see the tran­sis­tors in the sil­i­con, In­fra-Red, In Situ (IRIS) Inspection of Silicon « bun­nie’s blog and get 50 chips with a de­bug­ger and free de­vel­op­ment tools for the price of a cup of cof­fee and shipped free. It also means that world class en­gi­neers can de­sign MMUs onto chips that the gate keep­ers will never give a li­cense for.

He writes that this will hap­pen “not due to its ISA de­sign, but de­spite it,” and he means it as a mild in­dict­ment. Read it from here and it is not one. Winning on price and avail­abil­ity is not a lesser way to win. It de­cides who is in the room. An ar­chi­tec­ture that ar­rives in my coun­try at ten cents a part, with an open tool­chain and no li­cense to ne­go­ti­ate, puts em­bed­ded sys­tems within reach of peo­ple who were pre­vi­ously go­ing to watch some­body else’s demo board and con­sume thier prod­ucts with­out ever be­ing able to match what they have ac­cess to. That’s the power of free­dom, open­ness and is democ­racy in it’s truest sense.

That is a bet­ter rea­son than el­e­gance. and I want to tell Mr Grinberg, that the word priv­iledge he tosses around in his ar­ti­cle also ex­tends be­yond the ISA de­pend­ing on where you are in the world.

Nuff said.

Armstrong Subero is an em­bed­ded sys­tems en­gi­neer and pub­lished au­thor with Apress/Springer. He builds the Rovari RISC-V ed­u­ca­tion plat­form from Trinidad and Tobago.

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Reasoning scores keep climb­ing while per-to­ken com­pute keeps drop­ping. GLM-5.2 scores 99.2% on AIME 2026 with about 40 bil­lion pa­ra­me­ters ac­tive per to­ken. Qwen3.5 scores 91.3% with 17 bil­lion ac­tive. DeepSeek V4-Flash runs 13 bil­lion ac­tive. For scale, GPT-4 was ru­mored to run around 280 bil­lion ac­tive pa­ra­me­ters in 2023, and it could barely solve an AIME prob­lem. At the small end, Qwen3.5 9B fits in 6GB of VRAM quan­tized and roughly dou­bles the score of the next best model un­der 10B pa­ra­me­ters on Artificial Analysis’s in­tel­li­gence in­dex. If you only looked at math and code bench­marks, you’d con­clude that mod­els are get­ting smarter per pa­ra­me­ter at an ab­surd rate.

They are, on those bench­marks. Ask the same mod­els a plain fac­tual ques­tion and the pic­ture flips. On SimpleQA, a bench­mark of fac­tual re­call with no tools al­lowed, the cur­rent leader is Gemini 2.5 Pro at 53%, so the best re­call money can buy still misses half the ques­tions. The small mod­els barely reg­is­ter. Artificial Analysis mea­sures Qwen3.5 4B and 9B at hal­lu­ci­na­tion rates of 80 to 82% on its knowl­edge bench­mark, which means that when they don’t know a fact, which is most of the time, they make one up. Ask the 9B for the birth year of a mi­nor 19th-century math­e­mati­cian and you get a con­fi­dent, plau­si­ble, wrong an­swer. The pa­ra­me­ter count did­n’t drop for free. Labs are trad­ing world knowl­edge for rea­son­ing skill, and the trade is de­lib­er­ate.

What the pa­ra­me­ters were for

Facts take space. Research on knowl­edge ca­pac­ity (the “Physics of Language Models” se­ries has the clean­est mea­sure­ments) puts it on the or­der of two bits of fac­tual knowl­edge per pa­ra­me­ter. If you want a model that knows the birth year of every mi­nor Wikipedia fig­ure, the pop­u­la­tion of every Dutch mu­nic­i­pal­ity, and the ar­gu­ment or­der of every func­tion in every npm pack­age, you pay for that in weights, and it’s a big part of why fron­tier mod­els grew to tril­lions of pa­ra­me­ters.

Reasoning com­presses much bet­ter than facts do, be­cause it’s a rel­a­tively small set of pro­ce­dures ap­plied over and over: break the prob­lem into parts, track in­ter­me­di­ate state, check your own work, back­track when a step fails. Distillation and re­in­force­ment learn­ing on ver­i­fi­able tasks turn out to trans­fer those pro­ce­dures into small mod­els re­mark­ably well. Phi-4 is 14 bil­lion pa­ra­me­ters, trained heav­ily on syn­thetic text­book-style data, and it’s good at math and bad at trivia, which tells you ex­actly what its train­ing data con­tained. That mix used to look like a lim­i­ta­tion of the syn­thetic-data ap­proach. It now looks like the de­sign goal.

The knowl­edge that sur­vives the trade has a shape. These mod­els are gen­er­al­ists: they know a lit­tle about nearly every­thing and al­most noth­ing in depth. Ask one about PostgreSQL and it knows what it is, what it’s good at, and roughly how MVCC works, but ask which ver­sion added a spe­cific plan­ner fea­ture and you’re back to in­vented facts. That’s the right layer to keep in weights, be­cause breadth is what lets a model un­der­stand what a ques­tion is about, know what to look up, and judge whether a source is plau­si­ble. The depth is cheap to re­trieve and ex­pen­sive to store, so it’s the part that goes.

Facts rot, pro­ce­dures don’t

A fron­tier train­ing run takes months and costs hun­dreds of mil­lions of dol­lars, and the mo­ment it fin­ishes, the facts in­side it start go­ing stale. Library APIs change, prices change, peo­ple change jobs, and half of what a 2024 model be­lieved about the JavaScript ecosys­tem was out­dated be­fore the model shipped. Every fact you bake into weights has a shelf life, and the only way to re­fresh it is an­other train­ing run.

The pro­ce­dures don’t rot. Algebra worked the same way in 1970 as it does now, and so does break­ing a prob­lem down or spot­ting a con­tra­dic­tion be­tween two sources. A model that’s mostly pro­ce­dure and only lightly loaded with facts does­n’t age the way a knowl­edge-heavy model does. Its train­ing cut­off mat­ters much less, be­cause the cur­rent state of the world was never sup­posed to live in the weights in the first place. I think this is the best ar­gu­ment for the whole ap­proach: it de­cou­ples the ex­pen­sive, slow ar­ti­fact (the trained model) from the thing that changes daily (what’s true).

The har­ness car­ries the knowl­edge

If the model does­n’t know things, some­thing else has to, and that some­thing is the har­ness: re­trieval over a knowl­edge base, tool calls, web search, a filesys­tem full of docs. I wrote ear­lier that Rust is a har­ness for agents, a source of cheap ma­chine-check­able feed­back. This is the same shape from the other side. The model con­tributes rea­son­ing, and every­thing it rea­sons about gets sup­plied at run­time.

You can al­ready watch agents work this way. A cod­ing agent does­n’t need to have mem­o­rized your de­pen­den­cy’s API sur­face, be­cause it greps node_­mod­ules or reads the docs be­fore call­ing any­thing, and its an­swer is grounded in the ver­sion you ac­tu­ally have in­stalled rather than whichever ver­sion dom­i­nated the train­ing data. The re­call that used to be a fixed cost in every for­ward pass be­came an on-de­mand lookup.

A fron­tier model on your GPU

Follow the trend a cou­ple of years out and I think we get a model with fron­tier-qual­ity rea­son­ing, Fable-quality, that runs on a sin­gle con­sumer GPU. The com­pute half is nearly there. DeepSeek V4-Flash rea­sons with about 13 bil­lion ac­tive pa­ra­me­ters per to­ken, well within con­sumer-GPU range. What does­n’t fit is the other 271 bil­lion pa­ra­me­ters sit­ting in its ex­perts, and ex­pert lay­ers are mostly fact stor­age. That’s the part this whole trade makes op­tional. Strip the knowl­edge out and to­tal size shrinks to­ward ac­tive size, and a 20 to 40B model at 4-bit quan­ti­za­tion fits on the 24GB card that’s been sit­ting in gam­ing PCs since 2022.

The catch is that it won’t know much. Ask it a bare fac­tual ques­tion with no tools at­tached and the right be­hav­ior is to say it does­n’t know and go look it up. Paired with a de­cent har­ness, that’s most of what I use a fron­tier model for to­day, run­ning lo­cally with no per-to­ken bill and no data leav­ing the ma­chine.

This mostly solves hal­lu­ci­na­tion

The part I find most promis­ing is what this does to hal­lu­ci­na­tion. When a fact lives in weights, a wrong fact is un­find­able and un­fix­able. You can’t grep the weights, you can’t diff them against last month, and cor­rect­ing one er­ror means a fine-tune that might break who knows what else. The model states the wrong fact with the same flu­ent con­fi­dence as a right one, and there’s no ar­ti­fact to check it against.

When the fact lives out­side the model, a wrong an­swer has an ad­dress. The model cites a doc­u­ment, so you can open the doc­u­ment. If the doc­u­ment is wrong, you edit the doc­u­ment, and every fu­ture query gets the cor­rec­tion, which beats wait­ing for the next train­ing run by roughly a year. Retrieval does­n’t get you to zero, since a model can still mis­read a source or stitch two of them to­gether wrong, but a claim with a source is check­able and a claim from weights is­n’t. A wrong fact in a knowl­edge base is an or­di­nary data bug, the kind we al­ready know how to trace, fix, and write a re­gres­sion test for.

There’s a ver­sion of this fu­ture where the model card stops list­ing a knowl­edge cut­off at all, be­cause what’s left in the weights goes stale on a scale of years in­stead of weeks. The model just gets handed the world’s cur­rent state at run­time, the same way a CPU gets handed a pro­gram.

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August 10, 2026 Matt Lenhard 5 min read

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Where This Started

This is a fol­low-up ar­ti­cle to a piece I re­cently wrote about the to­ken re­lay mar­ket. Noticeably ab­sent from that piece was a men­tion of the rise of “token bro­kers” — peo­ple who buy un­used cred­its from star­tups and then re­sell them.

I first heard about to­ken bro­kers while chat­ting with a good friend of mine who was re­ceiv­ing of­fers for Anthropic to­kens at steep dis­counts.

It was­n’t just him, though. As I started talk­ing to more founders about what I was build­ing, they said the same thing: they were get­ting a lot of in­bound email from peo­ple look­ing to buy or sell off-mar­ket in­fer­ence.

Startups swap­ping cred­its is noth­ing new, and I knew this was hap­pen­ing in sev­eral startup fo­rums and groups, but this was when I re­al­ized that the mar­ket was be­ing com­mer­cial­ized.

So I did what any nor­mal per­son would do. I got the bro­kers’ email ad­dresses and started email­ing them to learn more.

Before my own out­reach, it’s worth see­ing what founders are ac­tu­ally re­ceiv­ing. Both of these were for­warded to me by friends.

I started by sourc­ing a few email ad­dresses from friends. The first two emails I sent bounced, but the third was a hit. Here’s a screen­shot of that con­ver­sa­tion:

What’s in­ter­est­ing is the amount of sup­ply. The seller was of­fer­ing $100k in spend per day.

They aren’t hand­ing out the provider keys di­rectly; in­stead, they act as a proxy that prob­a­bly picks from a pool of keys and for­wards the re­quest.

The Listings

Credit Marketplaces

There are a few web­sites pro­mot­ing credit bro­ker­ing as well. One of them, AI Credits, bills it­self as a credit mar­ket­place. For an­other fla­vor of the pure-play credit re­seller mar­ket­places, take a look at AICreditMart.

These sites of­fer cred­its at most of the ma­jor cloud and in­fer­ence providers.

AI Credits’ on­board­ing process is pretty straight­for­ward, and you can even se­lect your pre­ferred de­liv­ery method as the seller.

I went ahead and listed my cred­its, which are still pend­ing ap­proval.

Bulk Discounts

Another site that I found through a friend was CheapCredits. This site po­si­tions it­self as a router that is able to achieve its dis­counts through “bulk pric­ing.”

I no­ticed that this was a trend with a num­ber of sites that I be­lieve are act­ing as credit bro­kers. They pre­sent them­selves as be­ing able to of­fer dis­counts based on bulk pur­chases. Some other ex­am­ples in­clude Tokvana and Neokens.

Having spent time in the in­dus­try, I’d say that a 40% dis­count is very un­likely un­less you are one of the provider’s top cus­tomers. My hunch is that CheapCredits is ac­quir­ing the sup­ply in other ways.

CheapCredits even has a Data Processing Agreement for any­one look­ing to stay GDPR com­pli­ant.

The Message Boards

I checked where you’d ex­pect to find un­der­ground mar­ket­places.

Telegram had a few chan­nels, with one be­ing rel­a­tively ac­tive.

There are also spo­radic Reddit posts.

If you’ve been hang­ing out in any of the closed-off startup groups, I’m sure you’ve seen a num­ber of these posts as well.

So How Big Is This Market?

My rough es­ti­mate is that, across the sites, fo­rums, and re­sellers I looked at, there are prob­a­bly tens of mil­lions of these cred­its be­ing of­fered.

Unfortunately, when you try to of­fer nice things, abuse is­n’t far be­hind. Tokens have be­come a pseudo-cur­rency, and there is enough liq­uid­ity in the mar­ket to al­low for a lot of abuse. As we see the mar­ket turn and com­pa­nies be­come more aware of costs, crack­downs on this type of abuse prob­a­bly aren’t far be­hind.

Sources

Company and site names be­low are as they pre­sent them­selves pub­licly. Screenshots are from my own out­reach and from brows­ing the sites as a prospec­tive buyer and seller.

Previous piece: An Inside Look at the Relay Market Powering Token Resellers and Fraud

Credit mar­ket­places: AI Credits, AICreditMart

Bulk-discount routers: CheapCredits (cheapcredits.ai), Tokvana, Neokens

Direct out­reach: email ex­change with a bro­ker of­fer­ing $100k/day in spend

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16th August 2026

Friday’s big re­lease was Qwen 3.8 27B, an Apache 2 li­censed 27B pa­ra­me­ter vi­sion-ca­pa­ble LLM from Alibaba’s Qwen re­search lab. I’ve been look­ing for­ward to this one: 27B is an ex­cel­lent size for run­ning a model on a rea­son­ably specced lap­top, and its pre­de­ces­sor Qwen 3.6 27B was im­pres­sive.

Qwen’s self-re­ported bench­marks for this model are eye-open­ing. They show a boost from both Qwen 3.6 27B and the closed-weight Qwen 3.7-Plus, which was one of Qwen’s strongest mod­els of any size as re­cently as May this year. It will be in­ter­est­ing to hear what in­de­pen­dent bench­marks have to say about the model.

I’ve been run­ning the model on two dif­fer­ent ma­chines: my 128GB M5 Max MacBook Pro, and an NVIDIA DGX Spark. On both ma­chines I’m run­ning LM Studio and their 17GB Q4_K_M quan­tized build. I also tried us­ing llama-server di­rectly on the Spark.

Qwen’s doc­u­men­ta­tion de­scribes the model as de­fault­ing to xhigh for the rea­son­ing ef­fort, and the LM Studio GGUF I’ve been try­ing pre­serves that de­fault:

Qwen3.8 comes with of­fi­cial sup­port for rea­son­ing_­ef­fort, which can be used to ad­just rea­son­ing depth and con­trol cost:

xhigh (default): for com­plex tasks de­mand­ing thor­ough analy­sis

medium: bal­anc­ing ac­cu­racy and speed

low: ef­fi­cient rea­son­ing op­ti­miz­ing for speed and cost

Qwen3.8 comes with of­fi­cial sup­port for rea­son­ing_­ef­fort, which can be used to ad­just rea­son­ing depth and con­trol cost:

xhigh (default): for com­plex tasks de­mand­ing thor­ough analy­sis

medium: bal­anc­ing ac­cu­racy and speed

low: ef­fi­cient rea­son­ing op­ti­miz­ing for speed and cost

This is a hi­lar­i­ous de­fault. It’s ab­solutely not a good way to run the model, es­pe­cially on con­sumer hard­ware. I’ve been find­ing the re­sults ex­tremely en­ter­tain­ing.

I quickly ran into prob­lems with LM Studio’s de­fault con­text limit of 8,192 to­kens—Qwen was us­ing them all up think­ing about even the most mun­dane of prob­lems. I loaded the model with the full 262,144 max­i­mum con­text length and that prob­lem went away.

Here’s the pel­i­can rid­ing a bi­cy­cle SVG I got from my first at­tempt with that in­creased con­text length. It took 21 min­utes to gen­er­ate, us­ing 22,276 rea­son­ing to­kens to pro­duce 3,223 to­kens of out­put. You can read the rea­son­ing trace here.

This is by far the best pel­i­can SVG I’ve been able to gen­er­ate with a model that runs on a lo­cal ma­chine—and this Qwen is pretty small, just a 17GB file on disk. There’s a lot to like about this:

The bi­cy­cle frame is the right shape

It has legs on each side of the bike—that’s very rare

Good, clear pel­i­can pouch

The wings ex­tend to touch the han­dle­bars!

The mo­tion lines are be­hind, not in front

It has a taste­ful back­ground—nice sun, clouds, hill, flow­ers and grass.

Was that worth wait­ing 21 min­utes for? Absolutely not.

Here’s that same prompt run with rea­son­ing turned off—tran­script here. This one pro­duced 3,715 to­kens and took 137s—just over two min­utes.

And just for com­plete­ness, I used OpenRouter to run the same prompt through the much larger Qwen 3.8 2.4T-A95B (released last week) and got this snazzy an­i­mated SVG:

Your browser does not sup­port HTML5 video.

I said Qwen at xhigh has a ten­dency to over-think things, but how bad re­ally is it?

I tried a much sim­pler prompt, again with that de­fault ex­tra high set­ting:

draw an svg of a cir­cle

draw an svg of a cir­cle

Qwen’s rea­son­ing trace started like this:

The user is ask­ing for an SVG draw­ing of a cir­cle. Simple re­quest — but I want it to be a care­fully crafted piece. Let me make some­thing that goes be­yond just <circle>: a sin­gle self-con­tained SVG file with char­ac­ter — maybe a geo­met­ric “circle study,” with sub­tle an­i­ma­tion, lay­ered rings, and a dis­tinc­tive palette. Keep the scope right: they asked for an SVG of a cir­cle. So the core is a cir­cle. But I can add craft: con­cen­tric guide cir­cles (like a com­pass/​geom­e­try draw­ing), tick marks, a soft gra­di­ent fill on the main cir­cle, re­strained am­bi­ent mo­tion (a slowly ro­tat­ing dashed ring, puls­ing glow). Respect prefers-re­duced-mo­tion? That’s more for CSS; SVG SMIL or CSS in­side SVG will do. Palette op­tions: deep teal ink on warm pa­per? Or bold ver­mil­ion cir­cle on off-white with navy con­struc­tion lines — Bauhaus/compass-drawing vibe. Let me go with a “geometric study” look: cool slate back­ground, or bright pa­per white? Paper white is fine if it’s not the cream-and-ter­ra­cotta combo. […]

The user is ask­ing for an SVG draw­ing of a cir­cle. Simple re­quest — but I want it to be a care­fully crafted piece. Let me make some­thing that goes be­yond just <circle>: a sin­gle self-con­tained SVG file with char­ac­ter — maybe a geo­met­ric “circle study,” with sub­tle an­i­ma­tion, lay­ered rings, and a dis­tinc­tive palette.

Keep the scope right: they asked for an SVG of a cir­cle. So the core is a cir­cle. But I can add craft: con­cen­tric guide cir­cles (like a com­pass/​geom­e­try draw­ing), tick marks, a soft gra­di­ent fill on the main cir­cle, re­strained am­bi­ent mo­tion (a slowly ro­tat­ing dashed ring, puls­ing glow). Respect prefers-re­duced-mo­tion? That’s more for CSS; SVG SMIL or CSS in­side SVG will do.

Palette op­tions: deep teal ink on warm pa­per? Or bold ver­mil­ion cir­cle on off-white with navy con­struc­tion lines — Bauhaus/compass-drawing vibe. Let me go with a “geometric study” look: cool slate back­ground, or bright pa­per white? Paper white is fine if it’s not the cream-and-ter­ra­cotta combo. […]

Several min­utes later it pro­duced this ab­solutely beau­ti­ful an­i­mated cir­cle, which was en­tirely not what I had asked for!

Your browser does not sup­port HTML5 video.

My strong rec­om­men­da­tion: ig­nore that de­fault. Run Qwen 3.8 27B on low or even no rea­son­ing lev­els at first. It’s a great model, but wow that de­fault set­ting is a bad place to start.

It’s very good at bound­ing boxes

A fun way to test a vi­sion model is to see how well it can re­turn bound­ing boxes around items in a pho­to­graph. I’ve seen pre­vi­ous Qwen mod­els deal well with this, so I de­cided to put it to the test draw­ing bound­ing boxes around some pel­i­cans.

I’ve seen ask­ing for 0 – 1000 scale pro­duce good re­sults in the past. I tried this:

llm -a https://​sta­tic.inat­u­ral­ist.org/​pho­tos/​714731804/​large.jpg \ -m lm­stu­dio/​qwen/​qwen3.8 – 27b \ ‘Return JSON bound­ing boxes for the pel­i­cans in this photo, 0 – 1000 scale for each di­men­sion’

Here’s the rea­son­ing trace, which pro­duced this:

[ {“bbox_2d”: [195, 290, 370, 780], “label”: “pelicans”}, {“bbox_2d”: [445, 320, 675, 850], “label”: “pelicans”} ]

This is such a good match. Here are those boxes ren­dered on top of the photo:

Building a tool to la­bel bound­ing boxes

That vi­su­al­iza­tion of the bound­ing boxes was taken us­ing a new cus­tom tool that I had Qwen 3.8 27B build for me, run­ning of­fline on my lap­top.

I for­got to dial down the think­ing ef­fort so it was mas­sively over-en­gi­neered, but it did man­age to pro­duce this full in­ter­face from this sin­gle prompt:

[ {“bbox_2d”: [195, 290, 370, 780], “label”: “pelicans”}, {“bbox_2d”: [445, 320, 675, 850], “label”: “pelicans”} ]

Build an HTML page which has an in­put box for ac­cept­ing the URL to an im­age and a textarea for ac­cept­ing the above style of JSON. It ap­pends the im­age to the page, mea­sures its width and height, then treats the co­ords in the bbox_2d as scaled from 0 – 1000 and scales them against the ac­tual width and height, then it ren­ders la­belled boxes over the im­age.

[ {“bbox_2d”: [195, 290, 370, 780], “label”: “pelicans”}, {“bbox_2d”: [445, 320, 675, 850], “label”: “pelicans”} ]

Build an HTML page which has an in­put box for ac­cept­ing the URL to an im­age and a textarea for ac­cept­ing the above style of JSON.

It ap­pends the im­age to the page, mea­sures its width and height, then treats the co­ords in the bbox_2d as scaled from 0 – 1000 and scales them against the ac­tual width and height, then it ren­ders la­belled boxes over the im­age.

This screen­shot shows one of the fea­tures I did not ask for—a demo scene, for if you don’t have a pho­to­graph to test the tool with:

Here’s the rel­e­vant seg­ment of the think­ing trace, where it de­cided to draw its own pel­i­cans purely be­cause I had used the la­bel “pelicans” in the ex­am­ple JSON I gave it in the prompt:

Also a “load sam­ple” that uses a known im­age? Can’t de­pend on ex­ter­nal im­ages, but… the im­age URL in­put is user-pro­vided; I could add a “try with sam­ple” but­ton […] Hmm, I can draw a sim­ple scene on can­vas, ex­port it as a data URL, and load it into the im­age — that’s self-con­tained and demo-able! […] But the user’s co­ords are for an ac­tual pel­i­can im­age; a gen­er­ated place­holder can still demo the scal­ing. Generate a 1000x1000 place­holder: gra­di­ent wa­ter + two blob-like “pelican” sil­hou­ettes placed at the given bboxes (using the same scale — cute: sil­hou­ettes at the ex­act 0 – 1000 po­si­tions, show­ing the boxes align). This makes for a fun, self-con­tained demo. Keep it sim­ple: sky gra­di­ent, sun, wa­ter, two pel­i­can-ish shapes (ellipse body, cir­cle head, beak). Place at bbox cen­ters.

Also a “load sam­ple” that uses a known im­age? Can’t de­pend on ex­ter­nal im­ages, but… the im­age URL in­put is user-pro­vided; I could add a “try with sam­ple” but­ton […] Hmm, I can draw a sim­ple scene on can­vas, ex­port it as a data URL, and load it into the im­age — that’s self-con­tained and demo-able! […] But the user’s co­ords are for an ac­tual pel­i­can im­age; a gen­er­ated place­holder can still demo the scal­ing. Generate a 1000x1000 place­holder: gra­di­ent wa­ter + two blob-like “pelican” sil­hou­ettes placed at the given bboxes (using the same scale — cute: sil­hou­ettes at the ex­act 0 – 1000 po­si­tions, show­ing the boxes align). This makes for a fun, self-con­tained demo. Keep it sim­ple: sky gra­di­ent, sun, wa­ter, two pel­i­can-ish shapes (ellipse body, cir­cle head, beak). Place at bbox cen­ters.

(I’m slightly ner­vous that mod­els around the world might have a bias to­wards draw­ing pel­i­cans at any chance they can get, brought on by nearly two years of ex­po­sure to my own stu­pid bench­mark.)

Is all that over-think­ing nec­es­sary? Maybe it is, at least a bit. I tried with rea­son­ing turned off and got this ver­sion, (transcript here), which nearly works but shows the boxes in the wrong place:

So with­out rea­son­ing it did­n’t quite one-shot a work­ing tool. I’m sure it could get there with some fol­low-up prompts, but this is a good ex­am­ple of how rea­son­ing can make a dif­fer­ence.

Yes, it can drive cod­ing agents

One of the biggest ques­tions around lo­cal mod­els is whether or not they have enough horse­power to suc­cess­fully run a cod­ing agent loop. Coding agents re­quire long con­text, strong code gen­er­a­tion sup­port and re­li­able tool-call­ing. On pa­per Qwen 3.8 27B has all three of these, so is it up to the task?

My ini­tial ex­per­i­ments with Pi have been very promis­ing. I chose Pi be­cause it has a shorter sys­tem prompt than most other op­tions, mak­ing it a bet­ter fit for try­ing out smaller mod­els.

I con­fig­ured Pi to use Qwen 3.8 27B run­ning in LM Studio on the Spark (shared via tailscale serve) by adding this to ~/.pi/agent/models.json:

{ “providers”: { “spark”: { “baseUrl”: “https://​spark-18b3.tail68a31.ts.net/​v1”, “api”: “openai-responses”, “apiKey”: “dummy”, “models”: [ { “id”: “qwen3.8 – 27b”, “reasoning”: true } ] } } }

Then ran pi –provider spark –model qwen3.8 – 27b in my ~/dev/datasette folder and prompted:

how does auth work?

how does auth work?

After a se­quence of rea­son­ing and tool calls that ac­cessed a bunch of dif­fer­ent files it pro­duced this re­ply, which is very solid.

Just one prob­lem: I wanted to share that tran­script. So I pointed Pi and Qwen 3.8 27B at the JSONL tran­script file in ~/.pi/agent/sessions/–Users-simon-Dropbox-dev-datasette– and prompted:

Write Python code to con­vert this jsonl to mark­down

Write Python code to con­vert this jsonl to mark­down

And it built and tested this pi_j­son­l_­to_md.py, which did ex­actly what I needed. Here’s that ses­sion tran­script, pub­lished us­ing the tool that it cre­ated.

The quest for speed

So far this is all look­ing very promis­ing. We have a 17GB model that runs on high-end con­sumer hard­ware and can write code, drive tools, an­no­tate im­ages and gen­er­ally do every­thing that I need from an LLM for get­ting real work done.

There’s one very sig­nif­i­cant catch: it feels slow—es­pe­cially when it starts over-think­ing, but even with­out that it’s not par­tic­u­larly sprightly.

I’ve been get­ting around 15 – 30 to­kens a sec­ond from LM Studio. That’s not ter­ri­ble, but it’s slow enough that it’s go­ing to be hard to win me away from hosted API mod­els, which can re­turn re­sults a whole lot faster. Artificial Analysis track to­ken speed and show OpenAI 5.6 Sol at 74 to­kens/​sec­ond and 5.6 Luna at an im­pres­sive 184/second.

The good news is that the com­mu­nity have been ex­plor­ing ways to speed things up since the model was first re­leased two days ago.

One of the most promis­ing op­ti­miza­tions is baked into the model it­self. Qwen sup­ports Multi-Token Prediction, an ar­chi­tec­ture trick where a cheaper mech­a­nism guesses sev­eral to­kens ahead and the main model can then quickly ver­ify if the guesses were cor­rect. This can have quite a dra­matic ef­fect on in­fer­ence per­for­mance.

Based on this tweet from llama.cpp cre­ator Georgi Gerganov I tried run­ning the model with MTP like this on the Spark:

llama serve \ -hf ggml-org/​Qwen3.8 – 27B-GGUF:Q4_K_M \ -hfd ggml-org/​Qwen3.8 – 27B-GGUF:Q4_0 \ –spec-default \ –spec-type draft-mtp \ –reasoning-preserve

And sure enough, this gave me a sig­nif­i­cant boost. I had GPT-5.6 in Codex run a com­par­a­tive bench­mark on the Spark and the –spec-type draft-mtp server out­per­formed the LM Studio de­fault GGUF by around 72%.

I ex­pect we’ll see a whole lot more in­no­va­tion around serv­ing this model faster over the next few weeks. The MLX com­mu­nity likely have some tricks brew­ing as well.

Some ob­ser­va­tions

The fact that a 17GB file can do all of this stuff on my home ma­chines is a mir­a­cle. Once again, I’m de­lighted and amazed at how much progress lo­cal mod­els have made this year. A year ago this would have been com­pet­i­tive with the best and most ex­pen­sive of the pro­pri­etary mod­els—to­day it can run on a ca­pa­ble lap­top.

The only thing hold­ing this back from be­ing a daily dri­ver is per­for­mance. It feels pretty slow on both the M5 Mac and the DGX Spark. That’s the catch with these dense (non-Mixture-of-Experts) mod­els—they re­quire a whole lot of mem­ory band­width to per­form well, and nei­ther of the ma­chines I have ac­cess to are top per­form­ers in that re­gard.

The most im­por­tant thing about Qwen 3.8 27B is what it demon­strates. We can have an open weights gen­eral pur­pose model with a long con­text, ef­fec­tive tool call­ing, strong vi­sion abil­ity, and com­pe­tent code gen­er­a­tion, and we can fit the whole thing in just a 17GB file.

The mod­els at this size con­tinue to get bet­ter at an im­pres­sive rate. We don’t need to spend half a mil­lion dol­lars on dat­a­cen­ter-class hard­ware just to run a com­pe­tent model.

Talk to LittleLearner

The hosted 5B model, live in your browser. Open in a new tab ↗ if the chat does­n’t load be­low.

A con­trolled sand­box for study­ing how mod­els ac­quire knowl­edge

Modern LMs are trained on every­thing at once, so it is hard to tell whether a new skill was learned or merely elicited. We con­strain the train­ing dis­tri­b­u­tion it­self: an 88B-token cor­pus fil­tered to the U.S. el­e­men­tary-school cur­ricu­lum, with mod­els trained from scratch on it and matched un­fil­tered con­trols.

Dataset

LittleCurriculum

An 88B-token cor­pus dis­tilled from FineWeb-Edu through a five-stage fil­ter­ing pipeline aligned with Common Core stan­dards (K–5). Concepts, facts, and vo­cab­u­lary taught above Grade 5 are ex­plic­itly ex­cluded.

Models

LittleLearner

Three scales (0.6B / 1.3B / 5B) trained from scratch on LittleCurriculum: chat­table mod­els with an in­ter­pretable knowl­edge bound­ary. Each ships with a matched Unfiltered con­trol for clean com­par­i­son.

Findings

Elicitation, not ac­qui­si­tion

In our ex­per­i­ments, scal­ing, SFT+GRPO post-train­ing, and in-con­text learn­ing am­plify what the cur­ricu­lum taught, but none mean­ing­fully im­proves out-of-scope per­for­mance, in­di­cat­ing that the pre­train­ing fil­ter sets the ef­fec­tive ca­pa­bil­ity ceil­ing.

Model check­points

LittleLearner at three scales (0.6B / 1.3B / 5B), each with a matched Unfiltered con­trol shar­ing its ar­chi­tec­ture, to­kens, and recipe.

Base: the pre­trained model. GRPO: math spe­cial­ists post-trained on MathCAMPS; re­sponses may ex­hibit a ten­dency to­ward math-ori­ented out­put. Chatty: vari­ants tuned for gen­eral chat be­hav­ior.

Capability stays in­side the cur­ricu­lum

Can stan­dard in­ter­ven­tions push a model past what its pre­train­ing data taught it? With the bound­ary un­der ex­per­i­men­tal con­trol, we can ask cleanly. In our ex­per­i­ments, each in­ter­ven­tion am­pli­fies in-scope abil­ity; none of them mean­ing­fully im­proves out-of-scope per­for­mance.

Scaling

Scaling model size im­proves per­for­mance within the mod­el’s con­trolled knowl­edge ex­po­sure and ex­tends mod­estly to prob­lems along the same learn­ing tra­jec­tory, but yields lit­tle im­prove­ment on prob­lems re­quir­ing more ad­vanced ca­pa­bil­i­ties out­side the ex­po­sure.

MathCAMPS ac­cu­racy by grade, across model size

Post-training

Post-training through GRPO sig­nif­i­cantly boosts in-scope K–5 ca­pa­bil­i­ties, but fails to re­cover out-of-scope be­yond-K–5 ca­pa­bil­i­ties, even when train­ing with out-of-scope data.

Post-training am­pli­fies K–5, not the be­yond-K–5 gap

In-context learn­ing

In-context learn­ing with the prompts we test does not un­lock new rea­son­ing ca­pa­bil­i­ties in be­yond-K–5 for our trained 5B LittleLearner.

Accuracy by prompt­ing con­di­tion

What will you teach it?

Because LittleLearner’s train­ing ex­po­sure is ex­plic­itly spec­i­fied, be­hav­ioral and rep­re­sen­ta­tional changes can be re­lated di­rectly to the con­cepts you in­tro­duce. Three di­rec­tions we’re ex­cited about:

01

RL & dis­cov­ery

Can RL cre­ate ca­pa­bil­ity?

The prior is re­stricted to K–5, so ca­pa­bil­i­ties that emerge un­der RL can be at­trib­uted to the RL process it­self. A tractable proxy for re­ward-dri­ven dis­cov­ery.

02

Continual learn­ing

Watch a con­cept be­ing learned

Introduce neg­a­tive num­bers and mea­sure sam­ple ef­fi­ciency, re­ten­tion, and in­ter­fer­ence. Or probe be­hav­ior near the bound­ary: does it an­swer, ab­stain, or hal­lu­ci­nate?

03

Educational sci­ence

Machine vs. child learn­ers

Specified ex­po­sure en­ables con­trolled hu­man-model com­par­i­son. Do mod­els and chil­dren need sim­i­lar ex­po­sure to learn frac­tions, or make sim­i­lar er­rors on word prob­lems?

＋

Your turn

Bring your own ques­tion

A known bound­ary turns your idea into a clean ex­per­i­ment!

If you find this work use­ful

Please cite our pa­per:

@misc{littlelearner2026, ti­tle={Lit­tle­Learner: Language Models Under Pedagogically-Controlled Knowledge Exposure}, au­thor={Fan­fei Li and Jana Zeller and Manuel Prada-Corral and Thaddäus Wiedemer and Prasanna Mayilvahanan and Ryan Cotterell and Wieland Brendel}, year={2026}, eprint={2608.13545}, archivePre­fix={arXiv}, pri­ma­ryClass={cs.CL}, url={https://​arxiv.org/​abs/​2608.13545} }

For 11 years, from 1929 to 1940, the Soviet Union did not have week­ends. Instead, to in­crease pro­duc­tiv­ity cit­i­zens were al­lo­cated a day off in every seven at ran­dom. With 80% of the pop­u­la­tion at work on any given day, fac­to­ries never had to power down.

A let­ter from a dis­grun­tled worker to Pravda news­pa­per, pub­lished soon af­ter the im­ple­men­ta­tion of this new work­ing cal­en­dar, out­lined the prob­lems: “What is there for us to do at home if our wives are in the fac­tory, our chil­dren at school, and no­body can visit us?” the let­ter-writer asked. “It is no hol­i­day if you have to have it alone.” Parents found them­selves at home while their chil­dren were at school, or with kids un­su­per­vised while they were on shift. With no shared day off, ex­tended fam­ily gath­er­ings be­came im­pos­si­ble. The work­force be­came de­mor­alised and list­less, the pro­jected spike in pro­duc­tiv­ity never ma­te­ri­alised, and the pol­icy was first mod­i­fied, then aban­doned al­to­gether.

Eleven years, though. Is that not ab­solutely wild? A world with­out week­ends feels im­pos­si­ble. A world with­out Saturday Night Fever, with­out Manic Monday. We may no longer go to church on Sunday, but we still wor­ship the week­end. “The week­end looms large be­cause it rep­re­sents the tri­umph of col­lec­tive time over mar­ket time,” says Brad Beaven, pro­fes­sor of so­cial and cul­tural his­tory at the University of Portsmouth. “It is not just about rest, but about re­claim­ing au­ton­omy from the in­dus­trial clock.”

Saturday and Sunday, sa­cred to the dig­nity and hu­man­ity of work­ing peo­ple, are laden with mythol­ogy and cer­e­mony. From Cilla Black to Gary Lineker, the main char­ac­ters of our week­ends be­come gi­ants of the cul­ture. We ro­man­ti­cise the week­end, even the pro­saic bits — the dis­tant roar of a lawn­mower, the rat­tle of clas­si­fied foot­ball re­sults on the ra­dio. Everyone knows what a week­end means. The snap of a lap­top cover at 4.58pm on a Friday. The clink of the first pint glass and the smell of chips on the way home. The slow-mo­tion pace of a week­end pave­ment, the gear change from hus­tle to me­an­der. The rit­u­als have changed with the times, of course, and peo­ple-watch­ing at brunch on Saturday is now as much of a tra­di­tion as cook­ing a roast at home on Sunday. A week­end changes shape as you move through life stages, but in each it­er­a­tion it re­mains a shared ex­pe­ri­ence be­tween you and your peers.

“The week­end that I had when I was 20 was very dif­fer­ent from the week­end that I have now, at 40 and with kids,” laughs Pedro Gomes, pro­fes­sor of eco­nom­ics at Birkbeck, University of London and au­thor of the book Friday is the New Saturday. “When you are young, you are bond­ing with your friends, and then when you get older, you might be with your fam­ily. Eventually, you might be with grand­chil­dren. We move through dif­fer­ent man­i­fes­ta­tions of the week­end, in our life­times.”

But it can be hard to pin down, these days, where a week­end be­gins and ends. Laundry gets done on a work-from-home Friday, but emails are an­swered on Sunday. A shop­ping splurge is as likely to be a cheer-up treat on your phone af­ter a tough Wednesday as a Saturday out­ing. The tra­di­tional Saturday 3pm foot­ball kick-off has been stretched across the tele­vi­sion sched­ules all the way to Monday evening. The four-day week — first pre­dicted by Richard Nixon, of all peo­ple, in 1956 — has be­come a re­al­ity in the Netherlands, with peo­ple work­ing an av­er­age of 32.1 hours. Friday is forg­ing ahead with a quiet se­ces­sion from the work­ing week with­out any­one sign­ing off the pa­per­work. It raises the ques­tion: what even is “the week­end” in 2026?

Remarkably, the con­cept of the week­end as we know it is only 100 years old. In 1926, Henry Ford changed the shape of the week, an­nounc­ing that the work­ers at his fac­to­ries would now do five eight-hour days in­stead of six, with no cut in pay. Ford did not in­vent the week­end — the idea had been bub­bling un­der for a cen­tury, in cam­paigns by trade unions, re­li­gious groups and pro­gres­sive em­ploy­ers — but by putting his con­sid­er­able in­dus­trial weight be­hind it, the two-day chunk of free­dom was born.

“It is high time to rid our­selves of the no­tion that leisure for work­men is ei­ther lost time or a class priv­i­lege,” Ford wrote in his com­pa­ny’s Ford News in October 1926. Ford’s in­no­va­tion was in part a re­sponse to his ear­lier in­ven­tion: the as­sem­bly line, which had in­creased pro­duc­tiv­ity but ex­hausted work­ers, with ab­sen­teeism up to 10% in fac­to­ries; in his ar­ti­cle he did not dis­guise that there was self-in­ter­est in­volved. “People who have more leisure re­quire more trans­porta­tion in ve­hi­cles,” he con­tin­ued. The new fash­ion for day trips was an ef­fec­tive mar­ket­ing de­vice to sell cars. Meanwhile, his fac­to­ries main­tained a steady level of pro­duc­tiv­ity de­spite the re­duced hours. In 1938, faced with ris­ing un­em­ploy­ment lev­els in the Great Depression, the five-day week was of­fi­cially adopted across the US.

On this side of the Atlantic, Boots the Chemist was the pi­o­neer. In 1933, the com­pany opened a new fac­tory in Nottingham, which proved so ef­fi­cient that there was soon a sur­plus of stock. Reluctant to lay his staff off with un­em­ploy­ment run­ning at 25%, John Boot ended the Saturday morn­ing shift, re­duc­ing hours for the 5,000-strong work­force with­out cut­ting wages. It was com­mer­cially suc­cess­ful, re­sult­ing in lower rates of ab­sen­teeism, and was adopted as Boots pol­icy in 1934. A gov­ern­ment in­quiry led by Richard Redmayne — great-grand­fa­ther, fun fact, of ac­tor Eddie — pub­lished a “review of the ex­per­i­men­tal work­ing of the five days week”, which noted “an im­prove­ment in sta­mina and an­i­ma­tion of the em­ploy­ees aris­ing from the phys­i­o­log­i­cal and psy­cho­log­i­cal ef­fects of a long week­end’s rest and re­lax­ation”. Slowly, the week­end gath­ered mo­men­tum. In the UK, “the early 20th-century ver­sion of the week­end was gen­er­ally recog­nised as a half-day Saturday and Sunday off,” says Beaven. “The full two-day week­end only re­ally be­came widely adopted af­ter the sec­ond world war.”

Before in­dus­tri­al­i­sa­tion, there was lit­tle con­cept of con­sec­u­tive days of leisure, be­cause an­i­mals and crops could not be so long ne­glected. Work was dic­tated by the weather and the sea­sons, the clock mat­ter­ing less than the sun. Factories, with their whis­tles and watches, changed the way time worked. A mech­a­nised drum­beat of shifts and pay­days drowned out the old rhythms of sea­sons and saints days.

As work be­came more rigidly or­gan­ised, the pos­si­bil­ity emerged that leisure could be, too. Time off was­n’t merely a con­ces­sion to work­ers, but also an en­gine of con­sumer cap­i­tal­ism. People with week­ends would go to sports sta­di­ums, buy pic­nic bas­kets and new clothes, need cin­ema tick­ets. By the late 20th cen­tury, with the ar­rival of cheap flights, this had evolved into the mini­break: a minia­ture hol­i­day, de­signed to fit into a week­end. Leisure time, once the op­po­site of the econ­omy, be­came part of it.

For gen­er­a­tions, the British week­end re­volved around one im­mov­able ap­point­ment: the 3pm Saturday kick-off. “The week­end is about rest, but it is also about pas­sion,” says Gomes. “Most of us are not lucky enough to be pas­sion­ate about our jobs, but at the week­end we can fol­low our pas­sions.” The link be­tween foot­ball and the best day of the week is in­trin­sic to the na­tional love af­fair with the sport. It is prob­a­bly partly be­cause foot­ball sym­bol­ised the best bit of the week­end that we ended up so ob­sessed with it.

Morals — and the ab­sence of them — have al­ways been a theme of the week­end. When Sunday, a time of wor­ship, was the only day off, skilled work­ers de­vel­oped a habit of ex­tend­ing their free time into “Saint Monday”, by not turn­ing up for work af­ter a par­tic­u­larly en­thu­si­as­tic day of drink­ing. Saturday af­ter­noons off — and then the whole day — were granted by em­ploy­ers partly in the hope of bring­ing the hang­overs for­ward by a day. Victorian re­form­ers, ob­sessed with drunk­en­ness, hoped that free Saturdays would en­cour­age re­spectable recre­ation: or­gan­ised sport, gar­den­ing, fam­ily out­ings. Ford, a ve­he­ment sup­porter of Prohibition, be­lieved that the il­le­gal­ity of al­co­hol made his move to­wards a two-day week­end safe. (“A day off is no longer a day drunk,” he said.)

The re­al­ity has never been quite so clean-cut. Weekends are naughty and nice, both bad be­hav­iour and Sunday best. These are the days for shop­ping splurges and drink­ing sprees and hook-ups, but also for penance, whether by parkrun, DIY or ac­tual prayer. Though per­haps less so the prayer bit: around one in three Britons at­tended church reg­u­larly in 1900, ac­cord­ing to the National Centre for Social Research; now, this is num­ber is around one in 20. Two mo­ments stand out in the story of how Sunday lost its spe­cial place as a day, if not of wor­ship, then of higher pur­pose. In 1994, the Sunday Trading Act al­lowed large shops in England and Wales to open on Sundays. Then, a quar­ter of a cen­tury later, pan­demic lock­downs broke the now-frag­ile bonds be­tween churches and their com­mu­ni­ties, and left many older parish­ioners with a wari­ness of gath­er­ing in ill-ven­ti­lated churches that might be bad for the health, even if good for the soul.

The late, great Maggie Smith had, as she so of­ten did, the best line. “What is a week­end?” she asked, in Downton Abbey, with the en­ti­tled be­wil­der­ment of a dowa­ger count­ess for whom in­come is spoon­fed in sil­ver from birth, not doled out in a brown en­ve­lope on a Friday night. The week­end is time carved out of, and in ten­sion with, some­one else’s own­er­ship of your time. Not to men­tion that for aris­to­crats, who had ser­vants for every­thing from lay­ing fires to but­ton­ing their dresses, every day was a day of leisure. The week­end feels like a cor­ner­stone of civil­i­sa­tion, of democ­racy, be­cause it mat­ters most to those who spend the ma­jor­ity of the week fol­low­ing or­ders in­stead of giv­ing them.

af­ter newslet­ter pro­mo­tion

These days, the up­stairs-down­stairs di­vi­sion is be­tween the hy­brid work­ers and those whose work can never be done re­motely. An age-old di­vi­sion be­tween shift work and the rel­a­tive flex­i­bil­ity of white-col­lar jobs has deep­ened. Thinkers like Liselotte Lyngsø, found­ing part­ner of the Copenhagen-based con­sul­tancy Future Navigator, have ar­gued that the work­force is split­ting into “time own­ers” — knowl­edge work­ers who in­creas­ingly choose where and when they work — and “time slaves”, whose jobs, like health­care, man­ual shift work or gig econ­omy roles like Amazon or Deliveroo dri­ving, re­mains stub­bornly tied to the clock.

For the “time own­ers”, Friday is rapidly turn­ing into the mod­ern Saint Monday. It is an in­creas­ingly open se­cret that the last day of the work­ing week has be­come, for re­mote work­ers, an un­of­fi­cial half shift: cal­en­dar tech­ni­cally open, but both brain and lap­top mostly on standby. When Gomes ran a six-month trial of four-day-week work­ing in Portugal in 2023, with more than 41 pub­lic and pri­vate firms in­volved, “organisations that can’t re­duce hours — a nurs­ery, for ex­am­ple — worked in shifts with a dif­fer­ent day off within each week. Other firms made the de­ci­sion to cut Friday out. Whenever their week­day off was, we found that many em­ploy­ees ap­proached that day a lit­tle dif­fer­ently, us­ing it to get ‘life ad­min’ done so that they could have their Saturdays and Sundays free. The week­end is­n’t just about the num­ber of hours, it is also a co­or­di­na­tion de­vice for com­mu­ni­ties to con­nect.”

As with Saint Monday, “Skiveday Friday” has proved in the UK to be stub­bornly, if silently, ad­hered to. In 2024, Transport for London ran a three-month trial scrap­ping peak fares on Fridays ex­plic­itly in or­der to lure com­muters back into cen­tral London. It failed, Fridays re­main­ing quiet, de­spite the dis­count. For the lap­topped-classes, there has been a shift in what Friday is for. Several ma­jor rail com­pa­nies, in­clud­ing LNER and Avanti, have now made Fridays an en­tirely off-peak day like Saturday and Sunday, ac­knowl­edg­ing that the week­end now be­gins ear­lier for many.

Though it should be noted that, for those same work­ers, Saturday and Sunday them­selves are un­der con­stant siege from the mis­sion creep of work emails and con­tactabil­ity out­side work­ing hours in the age of the smart­phone. “These are two sides of the same coin,” says Gomes. Work in­ten­si­fies, and the week­end ex­pands in or­der to ab­sorb the pres­sure. “The speed of com­mu­ni­ca­tion means that we now live with con­stant in­ter­rup­tions, and it is hard to find space ei­ther for deep work or for real rest. And yet we con­tinue to struc­ture the work week in much the same way as we did 100 years ago. It is no sur­prise that this is­n’t work­ing.”

Hence why a three-day week­end is be­ing sug­gested as a way to bal­ance the books. But is it all woke non­sense? A pie in the sky idea, dreamed up by a lazy work­force which no longer knows the mean­ing of hard work? The Green party sup­ports a “move to­wards” a four-day week, but other politi­cians are wary. In 2025, the Liberal Democrat-led South Cambridgeshire coun­cil, which had been op­er­at­ing a four-day week for two years, was at­tacked by the then lo­cal gov­ern­ment sec­re­tary, Steve Reed, who said that lo­cal gov­ern­ments “should not be pay­ing full-time wages for part-time work”. In April this year, James Cleverly an­nounced that a fu­ture Conservative gov­ern­ment would look to ban four-day weeks for coun­cil staff, de­nounc­ing the “push from the left of cen­tre in British pol­i­tics” to­wards a four-day week as “completely wrong”.

The 4 Day Week Foundation, which is lead­ing the drive for change in Britain, says that 56 out of the 61 com­pa­nies who signed up for their four-day week pi­lot de­cided to stick with it af­ter the scheme ended. They cite an av­er­age of 35% in­creased rev­enue, and 57% de­cline in staff leav­ing rates, dur­ing the trial. There are now 260 com­pa­nies in the UK of­fi­cially signed up to the four-day week.

The di­vide — be­tween time shar­ers and time slaves, those work­ing five-day weeks, four-day weeks or more ad-hoc, less week­end-friendly shift work — is prob­lem­atic be­cause the week­end is de­signed to be shared. It’s where British cul­ture learned to gather, a col­lec­tive ex­pe­ri­ence. Every gen­er­a­tion has its own shared rit­u­als. Boomers love a Saturday night movie, gen­er­a­tion X are ob­sessed with Sunday lunch, mil­len­ni­als love to flock to a farm­ers’ mar­ket, while gen Z, for some rea­son, get their kicks stand­ing in line for baked goods that have gone vi­ral on TikTok. Even the Sunday scaries are made man­age­able by the knowl­edge that every­one out there is feel­ing the same. As a gulf widens — be­tween the time own­ers and time slaves, be­tween in­her­ited wealth and the in­creas­ingly weedy salary pipeline — the week­end starts to feel less like an ex­pe­ri­ence, and more like a nos­tal­gic mem­ory.

It is funny to think that, at first, tech­nol­ogy ex­panded our hori­zons. Railways gave or­di­nary peo­ple ac­cess to the coast; mass pro­duc­tion made bi­cy­cles and cars af­ford­able. It is only re­cently that tech­nol­ogy has mu­tated into the en­ergy vam­pire it is to­day, suck­ing the life from the week­end.

Only in the last decade has on­line au­to­bi­og­ra­phy be­come every­body’s un­paid side hus­tle, so that your friends have al­ready seen your hol­i­day pho­tos be­fore you meet them for brunch. It is a very mod­ern phe­nom­e­non that our friend­ships have mi­grated on­line, this week’s boy drama or fam­ily quar­rel al­ready de­bated at length in text bub­bles and voice notes with­out the need to meet in the pub. Add to this the post-pan­demic nor­mal­i­sa­tion of the “soft com­mit­ment”, in which mak­ing plans and can­celling them have be­come two parts of the same so­cial rit­ual and “I’ll see how I feel” has be­come an ac­cept­able RSVP, and we have be­come less good at a core el­e­ment of the week­end, which is ac­tu­ally see­ing other peo­ple.

Perhaps we should­n’t fret so much. Ever since we in­vented the week­end, we have been anx­iously tak­ing its pulse. The Victorians wor­ried that work­ers would waste their pre­cious leisure days drink­ing. Mid-century Britain feared tele­vi­sion was keep­ing fam­i­lies in­doors. Today’s con­cern is that we spend Saturday scrolling Instagram and Sunday an­swer­ing emails. The rit­u­als change; the sus­pi­cion that the week­end is be­ing ru­ined re­mains. Yet the week­end sur­vives, per­haps be­cause it has never de­manded per­fec­tion. The lie-in and the late night are equally valid forms of re­sis­tance. Its holi­est rites — the greasy fry-up, the foot­ball ter­race, Saturday-night shiny-floor tele­vi­sion — are glo­ri­ously un­re­fined. The week­end is where a good time still trumps good taste.

The Soviets dis­cov­ered that a day off is not much use un­less other peo­ple are off, too; Ford un­der­stood that work­ers needed time in which to be­come them­selves again, even if he hoped they would spend it buy­ing cars. At 100, the two-day week­end is fray­ing at the edges, leak­ing into Friday and nib­bled away by Sunday night emails. The week­end will keep chang­ing, be­cause work keeps chang­ing. But it will still al­ways be the best thing work ever in­vented.