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I think this example got here by inertia from the previous PR
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| scores = all_scores[0].clone().detach() | ||
| # Best-of-N Sampling. | ||
| scores_mask = scores != -1 |
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I think we need to merge changes from your last PR in
maxreciprocate
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Looks slick!
(One thing I've noticed that while score increases much faster on the training set with increase of num_return_sequences this doesn't necessarly yield better score on the test set. Do you have perhaps an example or parameter setting where it does so?)
| self.push_to_store(ppo_rl_elements) | ||
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| @staticmethod | ||
| def get_topk_indices(input_tensor, window_size: int, k: int, device): |
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Nit: maybe docstring should be added specifying that this isn't the same as regular topk but rather a topk overw window_size
Good point, the benefit of BoN trainings seems to be problem dependent. I've seen the most benefit during training on problems where the model has a low pass@1 score. |
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@maxreciprocate If you're happy with this do you want to merge today? |
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@Dahoas There are some run differences when using the default config without BoN sampling, most notably for the randomwalks case: |
Let me look into why. |
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@Dahoas Not sure if that's the issue however, see: https://wandb.ai/sorry/trlx/reports/Difference-due-to-the-change-in-base_trainer-decode--Vmlldzo1MzE2OTg4 (+ some non-determinism) |
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