I am very curious whether omitting the KL penalty helps on narrow domains like this, and also whether doing so results in illegible reasoning. (From the samples in the post, it looks like it doesn't make reasoning illegible?)
>the 32B model’s response lengths collapsing, especially after reaching peak performance.
I would not have predicted this. Nor that it could collapse its response length to near zero yet lose only a few percentage points of accuracy. If you do SFT to get a model of the same size to solve these puzzles with no reasoning (just output answers directly), how good can it do?
As for response length, I think the model internalizes the logic and doesn't deliberate its answers through context creation. I don't think this is necessarily good for general reasoning, but for a specific task it would cut down inference costs. Just depends on what you're optimizing for. To encourage more general reasoning, I think a broader train and validation set would be helpful.
Apparently DeepSeek-R1 can switch between English, Chinese, and gibberish, and even the gibberish helps it think! That's fascinating, but all I can find is people saying it, nobody showing it.
https://gr.inc/question/although-a-few-years-ago-the-fundame...
In the dropdown set to DeepSeek-R1, switch to the LIMO model (which apparently has a high frequency of language switching).
I'm not sure about examples of gibberish or totally illegible reasoning. My guess is that since R1-Zero still had the KL penalty, it should all be somewhat legible - the KL penalty encourages the model to not move too far from what the base model would say in any given context.
I think there's still a lot to learn about reward functions (saw a team work w/ just correct output, and nothing else), if you should reward partial success (i.e. code compiles / math outputs a result) or just the final thing (i.e. test cases pass / correct answer) and so on.
Not to mention how to get downstream signals from e2e tasks (i.e. if an "agent" navigates to the correct webpage and finds a "cookie" or something, figure out how to reward all the intermediary steps out of that single binary signal).
And there's a lot to learn in using grammars & stuff w/ RL as well. The problem there is that the libraries are pretty wonky atm, some things work, some things need work, and RL in itself is pretty slow due to having to generate, update the model and generate again.
From what I can see (not rigorous): Claude 3.7 fails, ChatGPT with reasoning succeeds, DeepSeek with reasoning succeeds.
But of course the best way for a model to solve a problem like this is to translate it into a constraint satisfaction problem, and write out Python code to call a CSP solver.
Which means that when you asked it (e.g.) whether A is better than B (as a Decision Support System), it should write a program to decide it instead of "guessing it" from the network.
You are stating that, since the issue is general, LLMs should write programs to produce their own outputs, instead of their standard output.
I'm not sure what that means specifically. I don't agree overall. Only certain types of problems encountered by LLMs map cleanly to well-understood problems where existing solvers are perfect.
The results of this paper would indicate doing what they did, but online could return better results
With our training recipe this can be easily done by accumulating the gradients across the entire batch and only doing one step with optimizer before sampling more responses.
In our experiments, however, we found the advantages of doing multiple gradient steps outweighed any potential drift in policy.
Ultimately the online-ness of data is on a spectrum and while more online data is better, other factors may be more important.
Bit pedantic, but amusing thought; wouldn't that imply that asynchronous actor critic is an offline training methodology?
Then group relative advantages are calculated. If you have 16 different responses and the average accuracy is 0.5, then you subtract that from each reward and divide by the standard deviation. Say it's also 0.25. Then the advantage for our example would be (0.25 - 0.5) / 0.25 = -1.
The advantages are then used to increase (or decrease) the probability of sampling those tokens again. Since our example was negative, we penalize the model for underperforming with that response.
Would be super interesting to see which one is more data-efficient!
“ We discovered that meaningful performance improvements, as high as 10–15%, can be achieved with as few as 16 training examples.”
In particular, did you need to change the hyperparameters much, and did this limited recipe show different improvements for the larger vs smaller models? Also, how did you select these 16 examples?
We only tested this with the 14B model. You can see the run here:
https://wandb.ai/bradhilton/rl-experiments/runs/062
Performance peaked after 21 iterations at 45% accuracy instead of the final 59%, but still a significant increase on very few samples.
Something interesting I noticed in the responses was that for shorter puzzles it would make deductions, building up a set additional "clues" for itself, before answering the question. However, for harder puzzles with more clues it would often merely repeat all the given clues and then try to directly answer the questions.
Maybe some form of curriculum learning would help, starting with easier puzzles and progressing to more challenging ones.
Other ideas to explore include:
- Distilling responses from stronger models - Encouraging exploration with entropy regularization or reward shaping - Training from base models instead of instruct models, like DeepSeek-R1-Zero
I think there's a lot of benefit to discovering a training regime that allows small specialized models to do extremely well in one narrow task; if we can figure out how to make small models that beat SOTA on a specific task and are cheap to train and run, that's in some ways a more useful outcome than a very large model that is good at many tasks (but is more expensive to run for each of them).
All problems require proficient reasoning to get a proper solution - not only puzzles. Without proper reasoning you can get some "heuristic", which can only be useful if you only needed an unreliable result based on "grosso modo" criteria.
Right, but the question is whether this is good enough. And what counts as "proper". A lot of what we call proper reasoning is still quite informal, and even mathematics is usually not formal enough to be converted directly into a formal language like Coq.
So this is a deep question: is talking reasoning? Humans talk (out loud, or in their heads). Are they then reasoning? Sure, some of what happens internally is not just self-talk, but the thought experiment goes: if the problem is not completely ineffable, then (a bit like Borges' library) there is some 1000-word text which is the best possible reasoned, witty, English-language 1000-word solution to the problem. In principle, an LLM can generate that.
If your goal is a reductio, ie my statement must be false since it implies models should write code for every problem - then I disagree, because while the ability to solve these problems might be a requirement to be deemed "an intelligence", nonetheless many other problems which require an intelligence don't require the ability to solve these problems.
Reasoning properly is at least operating through processes that output correct results.
> Borges' library
Which in fact is exactly made of "non-texts" (the process that produces them is `String s = numToString(n++);` - they are encoded numbers, not "weaved ideas").
> many other problems which require an intelligence don't require the ability to solve these problems
Which ones? Which problems that demand producing correct solutions could be solved by a general processor which could not solve a "detective game"?
You didn't like "what colour is the sky" (without looking), ok. "Given the following [unseen during training] page of text, can you guess what emotion the main character is feeling at the end?" This is a problem that a human can solve, and many LLMs can solve, even if they can't solve the detective puzzle. In case it doesn't sound important, this can be reframed as a customer-service sentiment-recognition problem.
(I'd instead guess that you tried to reply before the timer - which allows HN members to reply after a delay proportional to a function of the depth of the discussion tree - allowed you.)
> do is not reasoning
What some people do is «not reasoning», for lack of training, or for lack of resources (e.g. time - Herbert Simon's "satisficing"), or for lack of ability. I had to write since the late 2022 boom that "if the cousin you write about is consistently not using the faculty of intelligence you can't call her "intelligent" for the purpose of this discussion". I have just written in another parallel discussion that «There is a difference between John who has a keen ethical sense, Ron who does not exercise it, and Don who is a clinical psychopath with missing cerebral modules making it completely Values-blind» - of course if we had to implement ethics we would "backward engineer" John and use Don as a counter-model.
> can you guess what emotion
Let me remind you my words: «Without proper reasoning you can get some "heuristic", which can only be useful if you only needed an unreliable result based on "grosso modo" criteria». Is that problem one that has "true solutions" or one that has "good enough solutions"?
Let me give another example. Bare LLMs can be "good" (good enough) e.g. in setting capitalization and punctuation in "[a-z0-9 ]" texts, such as raw subtitles. That is because they operate without explicitly pondering the special cases in which it is subtle to unequivocally decide whether the punctuation there "should have been a colon or a dash", and such cases are generally rare, so heuristic seems to suffice.
Similar engines are useless and/or dangerous in all cases in which correct responses are critical. Important problems are those which require correct responses.
According to your definition of reasoning, which involves surely getting the right answer, no human does reasoning. Probably less than 1% of published mathematics meets the definition.
> Important problems are those which require correct responses.
There are many important problems where formal reasoning is not possible, and yet a decision is required, and both humans and LLMs can provide answers. "Should I accept this proposed business deal / should I declare war / what diagnostic test should I order?" We would like to have correct responses for these problems, but it is not possible, even in principle, to guarantee correctness. So yes, we use heuristics and approximate reasoning for such problems. Is an LLM "unreliable" or "dangerous" in such problems? Maybe yes, and maybe more so than humans, but maybe not, it depends on the case. To try to keep the point of the thread in focus, an LLM should probably not try to solve such problems by writing code.
Human "reasoning" (ie speech or self-talk that sounds a bit like reasoning) often outputs correct results. Does "often" fit the definition?
> Which problems that demand producing correct solutions could be solved by a processor which could not solve a "detective game"?
For example, "what colour is the sky right now?". A lot of people could solve this (even if they haven't looked outside), and so could a lot of language models, which can't solve this detective game.
No: "proper reasoning" is that process which given sufficient input will surely bring to a correct output owing to the effectiveness of its inner workings.
> what colour is the sky right now
That is not a general problem solver, and "output the most common recorded reply to a question" is certainly not a general problem solver, and the responses from the box indicated will easily be worthless for all special cases in which the question will make sense.
No. Let me reiterate: «"proper reasoning" is that process which given sufficient input will surely bring to a correct output owing to the effectiveness of its inner workings», given that enough resources are spent. I.e.: it is a matter of method.
And a processor that cannot solve the "detective games" shows lacking that method. (I.e.: the general capabilities that can be instanced in solving a "detective game" are required, though not exhaustive, for the reasoner.)
> we use heuristics and approximate reasoning for such problems
But we are expected to still use decent reasoning, even when bounded.
So: there may be no need to try and solve problems through writing code when the reasoning machine has the procedural modules that allow to reason similarly to running code, when such form of "diligence" is needed. When the decision is not that impactful (e.g. "best colour for the car"), let the decisor "feel"; when the decision will be impactful, I want that the decisor be able to reason.
Some do.
> whether you genuinely think LLMs should output code for most problems, or whether you were using that as a reductio against my initial statement
No, they should not. But proper reasoning is related to procedural operations like code.