Comment by nickysielicki
11 hours ago
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.
> 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
Not really, if anything it's closer to the opposite. The Bitter Lesson essay literally has this as an example:
> These researchers wanted methods based on human input to win and were disappointed when they did not.[1]
and
> Enormous initial efforts went into avoiding search by taking advantage of human knowledge, or of the special features of the game, but all those efforts proved irrelevant, or worse, once search was applied effectively at scale[1]
The actual bitter lesson is this:
> breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach.[1]
Applying to the "LLMs-for-chess" example the bitter lesson approach would be to put many, many more games into the LLM.
Does this work? People have trained fairly small LLMs that are competitive Stockfish at the ELO 1500-2000 level, eg: https://github.com/kinggongzilla/chess-bot-3000
This seems to be evidence that large LLMs probably don't have as much chess training data as Stockfish does.
[1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html
> These researchers wanted methods based on human input to win and were disappointed when they did not.[1]
This was/is basically a strawman though. Like maybe "human input winning" was desirable for chess masters but for computer science wonks? Not the point or the disappoint. It's always neats and scruffies fighting about using some kind of recognizable method (logic) instead of magic (ML).
> breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning.
More to OP's point I think: nowadays when someone wants to beat you over the head with the bitter lesson, they aren't as careful to include learning and search. They want to say learning leads to intuition (magic) whereby we can avoid work (logic/search), and maybe argue or assume from there that neats and scruffies is settled. TBF, something like reasoning in latent space does resemble intuition!
But the real lesson is confirmed every time we bother to check, and not very bitter for anyone. Search/learning/logic are ALL always necessary on any sufficiently difficult problems, and hybrids that interleave always outperform everything else. Stockfish being the example in this thread that different camps of absolutists would like to claim, but also all the MCTS examples, evolving examples, and new hybrids all the time. My favorite lately: https://arxiv.org/pdf/2511.08983
> This was/is basically a strawman though. Like maybe "human input winning" was desirable for chess masters but for computer science wonks?
Oh no!
The whole field was full of people whose entire career was built around the idea of developing smart priors.
To quote Wikipedia:
> For computer vision in particular, much progress came from manual feature engineering, such as SIFT features, SURF features, HoG features, bags of visual words, etc. It was a minority position in computer vision that features can be learned directly from data
This undersells the change though! David Lowe's reputation as the best image researcher in the world was based on his SIFT patent[1]
This approach worked until 30 September 2012.
That was a bitter day for many, many computer science researchers.
[1] https://en.wikipedia.org/wiki/Scale-invariant_feature_transf...
1 reply →
It's the exact opposite.
The bitter lesson is that simply scaling training on more games—including self-play—trumps any hand-crafted human input, whether that's fine-tuning on human commentary or clever engineering tricks.
Current models are just high-dimensional interpolation engines. The denser the data sampling, the more accurate the interpolation gets. Given a choice between denser sampling and anything else, denser sampling always wins. That is the bitter lesson.
Computer chess is the canonical example of this.
But the harness still matters.
In the case of stockfish, the harness is a tree search around the neural network evaluations.
Denser sampling only seems useful if the problem domain is in some way smooth - interpolatable. If you run it on a fractal problem domain you just learn more special cases. Chess is fractal.
I think you have it backwards.
The common mistake is to think “maybe if we use a blend of raw data and hand-crafted heuristics, we’ll get the best of both worlds!” But the bitter lesson says no, beyond a certain point it’s better just to use the data.
Thinking that an LLM might be able to improve on purely “big data” machine learning seems to me to be the same incorrect idea. Its “intelligence” is no more useful than human intelligence. The LLM is based on a massive data corpus, sure, but the amount of data specifically about chess in there pales in comparison to just playing billions of games of chess.
Also, training it on chess books is literally training it on human knowledge, and not the actual game, which is exactly what the bitter lesson says not to do.
> I think you have it backwards.
> maybe if we use a blend of raw data and hand-crafted heuristics
I don't follow. They're suggesting giving raw chess data to the LLM, no heuristics involved.
Maybe a future frontier LLM could approach the problem by first building its own stockfish, then applying the subsequent results
Or maybe an LLM could just tool call stockfish and doesn’t need to have more than a basic understanding of chess. The bitter lesson seems extraordinarily wasteful on the compute side.
Maybe a future LLM after that could approach the problem by first simulating a human brain, then learning from the ‘human’ gameplay.
Just kidding of course
Chess is a brute force search problem. Humans are not good at chess, even a small computer can beat Magnus Carlsen.
It would be better to compare models at how well they can write the code for chess engines, otherwise it's just saying that Fable is not a good CPU emulator, which is obvious.