Comment by jimmaswell
12 hours ago
This idea has failed to pan out time and time again - people have an instinct that hand-crafted finely-tuned specialized AI systems must be optimal, but throwing more scale and compute to something more generally smart always wins out. It's especially palpable just looking at the last few years of LLM's: a frontier model with all the world knowledge you can stuff in it and every tool at its disposal has always performed the best at all tasks. Suggesting otherwise has become an extraordinary claim requiring extraordinary evidence.
The Bitter Lesson is very popular right now. It seems true right now. It’s having its moment right now. That doesn’t actually mean it’s axiomatically true.
Commenter below gets it absolutely correct: stockfish, which runs on your 5 year old phone, is dramatically better at chess than Fable. Like, so much better that it’s not even remotely comparable. The theory of the Bitter Lesson, and it’s only a theory, is that LLMs could eventually outperform stockfish. It’s not true today and it remains to be seen whether it will ever be true. For now, specialized models are absolutely better at specialized tasks.
This seems really backwards. The Bitter Lesson is all about large data-based approaches vs hand-crafted ones, it doesn't say anything about language models not trained specifically for chess.
I can't find the comment you're referring to, but the latest versions of stockfish are based on neural networks trained on millions of games, so if anything the Bitter Lesson turned out true here.
The conclusion of the bitter lesson would be that a large language model trained on chess commentary as well as being trained on millions of chess games would outperform stockfish which is only trained on millions of chess games. There’s no evidence at this point that this is true.
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The Bitter Lesson says that the only things that scale are search and learning.
Stockfish is the best chess search engine we've got, and you can learn some good heuristics for chess search policy that will make time-limited chess search a lot more powerful. That's perfectly in line with the Bitter Lesson.
In contrast, LLMs playing chess are relying solely on learned behavior. The inference harnesses surrounding them aren't designed to do chess things, they're designed to do autoregressive token decoding, which isn't a search process. Reasoning traces can resemble a search process, but they're far less efficient - the LLM would have to work out each legal move, test each one, calculate a score, and simulate minimax over all of that. Assuming the LLM is smart enough to even do all that.
A hand-crafted approach can absolutely beat data if your approach unlocks more search and/or learning than the general solution.
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But isn't that really just about giving "front end" models more access to specialized tool libraries, which include models tuned to specific tasks? Like the first model says ah, we're being asked to code something, oh and we've been provided with some example code, let me invoke a tool call to my model the recognizes many languages, that model says that we're looking at ocaml. Okay, I better pass this off to my ocaml model which will decipher the supplied code and make a plan for what we do about the user's intent. The ocaml model recognizes that there are tests in the supplied code, let's have the special testing model have a look at the testing strategy and see how that fits in with what we just implemented, etc etc.
And perhaps at the end it all gets a single pass by a god-tier model for overall sanity and congruence, but the actual work, planning, coordination, and even user interaction was done by cheaper and faster agents of much more limited capability.
It’s kinda funny that your last paragraph is basically describing why sparse files, sparse matrices, etc. are used in other contexts.
It really is absurd to ask programming questions to a model also trained about the lifecycle of a fruit fly.
Instead of building small models from scratch, we train an enormous model and use ridiculous amounts of GPU memory. In the end, the whole thing is shoved into RAM because we don’t know where the useful parts are…
We certainly would know where they were if they were just in smaller models in the first place!
Good point.
Dumb AIs are needed for customer service. Most of that industry is still at "press 1 for sales, 2 for billing..." and needs something that will run locally on a 1U server.
> Most of that industry is still at "press 1 for sales, 2 for billing..." and needs something that will run locally on a 1U server.
Needs? Customers want something that immediately answers their question/solves their problem, but that’s far away, even ignoring the “run locally on a 1U server” and that that may not be in the company’s interest.
For many companies, that support line is a cost center, not a PR mechanism.
Also “Press 1 for sales, 2 for billing...” has the big advantage that it handles all accents, speech impediments, etc.
Long term I think a solution where a user’s agent trained on their voice, running on their phone communicates with the support agent of a company is where we will end up, and support phone lines will cease to exist.
Yes, and the technology to improve the interface you described is already available to run hundreds of concurrent instances on a 1U server. The barrier to entry is getting the people who manage those systems to care enough to implement something better.
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Yes, but an LLM will just call stockfish if it needs to play chess … sure if you arbitrarily constrain an LLM to use no tools it’ll suck at chess. But no one is using LLMs in isolation. Even consumer-grade, bone-stock ChatGPT has tools.
ChatGPT does not have stockfish as a tool it can call.
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Maybe depends on how you ask it? Directly, or let it write a chess program? I think the latter can yield way better results.
No. The bitter lesson is about capabilities. GP is talking about efficiency.
GP isn’t suggesting that focused narrow model(s) will be more capable than large model, but that many small focused models can have sufficient capability while being more optimal.
Also, the bitter lesson is just wrong. The bitter lesson is about hand tuned AI vs computational general methods. However in truth today’s AI uses both. We have general compute heavy models which require narrow expert instructions (eg tools internet docs).
LLMs would not be as good without expertly written context, and expert context without LLMs aren’t as good either.
> The bitter lesson is about hand tuned AI vs computational general methods. However in truth today’s AI uses both. We have general compute heavy models which require narrow expert instructions
The models are not even really trained bitter lesson-style anymore. That concept peaked during the era of pre-train scaling, back when it was thought that making a bigger and bigger GPT-3 would automatically solve all problems through prompting. In 2026, the most important part of training is post-training, which uses vast quantities of niche, hand-curated data to fit the models for specific tasks in domains like tax law.
I'd respectfully push back on the framing here.
If you look at value as purely the LLM output, then there's a valid argument that the best frontier models will always be better than fine tuned specialists. (I'm not convinced personally, but it's a defensible claim)
But that misses two dimensions: 1. The cost of acquiring that output 2. What is actually "good enough" for that specialist domain
Not every output needs to be the best to produce value.
And as specialist models increase in cost, their cost/value proposition goes down.
At some point, there's a threshold where cheaper, fine tuned models are "good enough" at the task and also substantially cheaper than the expert models.
That's where fine tuning helps.
Personally, I became a believer in fine tuning after fine tuning a 1B Qwen model as a second pass over my local voice transcription app, achieving excellent accuracy at ~zero token cost and waaaay lower latency than if I'd invoked my Claude subscription under the hood.
At some point, the idea of cost/benefit analysis in the software business turned into the benefit analysis. The amount of money going into the frontier LLM model game is fantastically ridiculous. Being much better than the free resources doesn’t even touch how much better they will have to be to justify the expense of creating them, let alone continually maintaining these services.
It’s not hard to fine tune a 4B parameter model to outperform GPT-5.x or Claude on a domain-specific task. You don’t even need GRPO or whatever the flavor of the month RL technique is either. I suspect what you’re saying is that massive frontier models are the best for doing big slow tasks on human time horizons—and I emphatically agree—but keep in mind LLMs are useful for quite a bit more than just multi-turn chatbots; and in those instances, throwing infinite compute and latency budgets at a problem is generally not a great way to find the “best” solution :)
> It's especially palpable just looking at the last few years of LLM's: a frontier model with all the world knowledge you can stuff in it and every tool at its disposal has always performed the best at all tasks. Suggesting otherwise has become an extraordinary claim requiring extraordinary evidence.
Absolutely false. At least when it comes to multimodal inputs, even a simple classifier will outperform the largest LLMs who still hallucinate details or don’t describe audio and images accurately.
And there’s also the issue of cost/inference speed. Running a trillion parameter model for all tasks will be incredibly costly, require a cloud API, while a tiny CNN can be run locally or at a cost multiple orders of magnitude lower.
This idea has not failed to pan out at all. I work for a startup that is exactly what GP described, and am set for life because of how wildly successful it is. Notably, we are successful, in a genuine sense of the word: we bootstrapped from running tiny models to larger and larger models on our own slowly improving fleet of GPUs, and now have millions in revenue without a single dime of outside investment. Conversely, you cannot call taking on ~1 trillion in debt and purchase commitments to scale "success". OpenAI and Anthropic are underwater financially. To be precise, they're in the Mariana Trench.
Wait, you actually found a viable counter to The Bitter Lesson? Please say more
Perhaps an analogy to Moore's law?
Bitter lesson #1: don't waste time optimizing code when a faster processor is around the corner.
What countered it: Moore's law stopped working.
Bitter lesson #2 similarly relies on scaling laws that might have diminishing returns wrt model runtime vs intelligence. Runtime matters for turnaround on the problem you're solving.
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This is a misunderstanding of either the bitter lesson or what was being claimed, on multiple accounts. Firstly, the bitter lesson is merely about human expertise-tuned algorithms vs. throwing raw compute at a domain. But, notably, it is still domain-specific. No matter how much compute you throw at training an LLM, it is never going to beat a Chess engine at Chess. If you give a Chess engine 1,000,000 compute units and a general-purpose LLM 1,000,000 compute units, the Chess engine is obviously superior at Chess; ergo, there is value in throwing compute units into training models for specific tasks. This is true for within several orders of magnitude of compute, in fact. It's also true that if you give the Chess engine 1000 compute units it'll still beat the all-purpose model with 1,000,000 units, so actually there's a lot of value in training for specific tasks.
Secondly, the bitter lesson is predicated on compute being cheap. There was a period where a hand-tuned algorithm informed by human expertise would outperform a raw alpha-beta search at Chess. Then compute got cheaper, and DeepBlue ascended to the top. Compute is now expensive again relative to the tasks being performed. We are absolutely still in a period where human expertise in training LLMs will outperform a naive approach with more raw compute.
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I think the gp found a viable counter to the ggp's version of the bitter lesson, which seems so extreme as to certainly exceptions. IE, they seem to say nothing but the latest frontier model is ultimately viable as an AI business.
The actual argument of the Bitter Lesson essay is pretty limited but people's interpretation of it has gradually drifted until it's seen as prediction that current LLM will reach AGI at a large enough scale.
The issue is that GP is misusing the bitter lesson. Yes, search + learn tends to be more effective than human rules based strategies, but that's not what's being considered here. The original claim is effectively that AGI isn't needed for most tasks and more value can be created by using search + learn to solve specific problems instead of applying general models to every problem. Then GP commented a non sequitur
Do you have a website?
Are you willing to say the same of the startup or are you still stealth?
Suggesting otherwise has become an extraordinary claim requiring extraordinary evidence.
VibeThinker 3B constitutes extraordinary evidence, IMO. The first such evidence I've seen myself. Very small model, very low literacy, almost no world knowledge, but it is as good at math and logical reasoning as models a hundred times larger.
The Bitter Lesson is a valid and trenchant observation about how about we got here, but I think it's a mistake to assume it tells us very much about where we're going. Too much has changed recently and is still doing so.
So theoretically, if you give that model the means to find information, ascertain the quality of said information, it could still reason its way to an proper answer?
Is this whole thing than maybe a read vs write optimisation again? Spent more time and effort training more knowledge into the model upfront and get it out in a single question instead of training a small model and needing more steps to answer the same question?
> VibeThinker 3B constitutes extraordinary evidence.. math and logical reasoning
Any similar model aimed at coding?
A >10B model for mass spawning/swarming and reporting back to a larger model
To some extent VT 3B is intended as a coding model (see https://old.reddit.com/r/LocalLLaMA/comments/1u7dzdr/scaling... ). It reportedly does well on leetcode-type problems, although I didn't check that myself.
I wouldn't use it for anything important without heavy supervision, as it's very weak outside its specialty. Not ideal for instruction-following tasks.