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Using GRPO to Beat o1, o3-mini and R1 at “Temporal Clue”
- kcorbitt 2y agoOne of the authors here. Happy to answer any questions about our methods/results!
- bydgjohc 2y agoAny hypotheses on why the performance dropped suddenly while training?
- a_wild_dandan 2y ago[flagged]
- deleted 2y ago[deleted]
- bradhilton 2y agoHi, other author here. I think the models converged on shallow/greedy strategies that improved performance up to a point, but are ultimately shortsighted, especially for harder puzzles. 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
- kiratp 2y agoIs my understanding here correct? Could this be the reason? https://news.ycombinator.com/item?id=43287312 https://news.ycombinator.com/item?id=43287312
- bradhilton 2y agoAs for why they dropped suddenly, I don't really know. Sometimes models develop degenerate behaviors, but even when forking from the best checkpoint and lowering the learning rate or changing other hyperparameters, performance stills drops. It's as if its fate has already been sealed many iterations ago.
- snovv_crash 2y agoDo you have any other logic puzzles you could use to see if the performance generalises?
- kcorbitt 2y agoTo be honest, I don't expect the performance to generalize to other task types with this specific training regime. If we had a panel of like 30 logic puzzles and cross-trained against all of them simultaneously it might though. 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).
- ekidd 2y agoOnce the problem gets narrow enough, do you risk training a model that reinvents a straightforward classic algorithm at far higher cost?
- bradhilton 2y agoWell, in this case there is a much more straightforward method with the same CP-SAT solver used to create the puzzles. This is more of a fun experiment to see if we can train LLMs to solve these kinds of logical deduction problems.
- shinryuu 2y agoThe question to me if you can call that deduction in that case. Isn't it just a type of pattern matching that fits this particular task?
- mdp2021 2y agoCan I just wholeheartedly congratulate you for having found a critical benchmark to evaluate LLMs. Either they achieve 100% accuracy in your game, or they cannot be considered trustworthy. I remain very confident that modules must be added to the available architectures to achieve the "strict 100%" result.
- deleted 2y ago[deleted]
- pama 2y agoCan you elaborate on this point: “ 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?
- bradhilton 2y agoNo meaningful changes to the hyperparameters, just changed the tasks per iteration to 16 and trained on the same first 16 training tasks each iteration. We only tested this with the 14B model. You can see the run here: https://wandb.ai/bradhilton/rl-experiments/runs/062 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.
- pama 2y agoThanks.
- malcolmgreaves 2y agoPlease define an acronym the first time you use it in the body text. I had to scroll about 20% the way through your article just to understand the title.
- bradhilton 2y agoGreat point! Thanks for the feedback.
- bradhilton 2y agoWe updated the first paragraph to define the acronym. Thanks again for the feedback!
- behnamoh 2y agothis is the same team that a few months ago here on hacker news talked about how to do fine-tuning on large language models, and then made it close source.
- Imnimo 2y ago>To speed up our experiments, we omitted the Kullback–Leibler (KL) divergence penalty, although our training recipe supports it for interested readers. 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?
- bradhilton 2y agoYeah, it may help. In this paper[1], the author used a KL penalty of 0.01 for general tasks and 0.001 for mathematical. I tend to think it's probably not very important unless you're trying to optimize for human preferences. 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. [1] https://arxiv.org/html/2501.03262v1 https://arxiv.org/html/2501.03262v1
- jstanley 2y agoI keep seeing people mention "illegible reasoning" but I'd be fascinated to see an example of what it actually looks like. Do you have any examples? 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.
- Imnimo 2y agoHere's an example of language switching: https://gr.inc/question/although-a-few-years-ago-the-fundamental-facts-about-the-milky-way-seemed-fairly/ 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.
- Tostino 2y agoI couldn't quickly find it by searching your github, but what layers did you end up targeting for training? Would be interesting to see an ablation on targeting different sets of layers (train only attention layers, freeze the first 30% of the layers and train the remaining 70%, etc).
- bradhilton 2y agoWe trained all the parameters. Those would definitely be interesting ablations. I would also like to see how much of a performance hit we would take with PEFT methods like LoRA.
- layer8 2y agoGRPO = Group Relative Policy Optimization https://arxiv.org/abs/2402.03300 https://arxiv.org/abs/2402.03300
- randomcatuser 2y agoWait, what's the difference between using GRPO and traditional fine-tuning of Qwen using your provided dataset? Would be super interesting to see which one is more data-efficient!
- bradhilton 2y agoGreat question! So the dataset includes prompts and solutions, but no "gold" answer per se to use for SFT. You could sample responses from larger models and then train the smaller model on their answers, but as outlined in the benchmarks there is still a lot of headroom on this task and I wouldn't expect that to get the same results. At the very least you would probably want to do rejection sampling to discard bad results. It would definitely be a good experiment!
- dsffsad 2y ago[flagged]
- bionhoward 2y agoThis looks impressive but I’m concerned, is it fair to “teach to the test” by fine tuning the Qwen model with RL on the test task, while the other models in the comparison are not fine tuned on the test task?
- bradhilton 2y agoYeah, the takeaway shouldn't be "our model is smarter," but that we were able to train weak models to as good or better than the best for this specific task. Depends on what you're doing, but sometimes that is enough.
- machiaweliczny 2y agoWould be great if some details given about how exactly model is penalized for staying off-track.
- bradhilton 2y agoThe model is rewarded for accuracy. For each puzzle there are a few multiple choice questions. If it got 1 out of 4 correct, for example, its reward would be 0.25. 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.
- kiratp 2y agoUnless I’m missing something this isn’t online RL. They are collecting outputs in one pass and then doing a separate offline GRPO training run on those. The results of this paper would indicate doing what they did, but online could return better results https://arxiv.org/abs/2402.04792 https://arxiv.org/abs/2402.04792
- bradhilton 2y agoTechnically yes, only if you do a gradient step with data sampled from the exact same weights is it an online step. 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.
- fc417fc802 2y ago> only if you do a gradient step with data sampled from the exact same weights is it an online step. Bit pedantic, but amusing thought; wouldn't that imply that asynchronous actor critic is an offline training methodology?
- bradhilton 2y agoYes, pedantically, it is! But as I said, everything's on a spectrum. Online-ish data can still work just fine.
- Liwink 2y agoCan you please share the training cost?
- bradhilton 2y agoWe used about 58 hours on 4xH100s and about 19 hours on 8xH100s to get the very best result with the 32B model. We trained for about another 16 hours before finishing the run, but we could have stopped earlier after it was apparent the model was regressing. Actual dollar costs are provider dependent.
- jmmcd 2y agoThese puzzles probably have more in common with "Zebra puzzles" (eg https://www.zebrapuzzles.com/ https://www.zebrapuzzles.com/) than Cluedo (USA Clue) itself. I've been doing some one-off experiments with Zebra puzzles recently. All the reasoning models generate an enormous batch of text, trying out possibilities, backtracking, and sometimes getting confused. 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.
- mdp2021 2y ago> But of course the best way for a model to solve a problem like this is to translate it 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.
- jmmcd 2y ago> since the issue is general 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.
- mdp2021 2y agoI am stating that since the ability to solve those puzzles is critical in an intelligence, and the general questions I can think of require an intelligence as processor, if to solve those problems the LLMs "should write code" then in general they should. 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.