69 lines
2.6 KiB
Markdown
69 lines
2.6 KiB
Markdown
---
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library_name: transformers
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tags: []
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---
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This is a process-supervised reward (PRM) trained on Mistral-generated data from the project [RLHFlow/RLHF-Reward-Modeling](https://github.com/RLHFlow/RLHF-Reward-Modeling)
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The model is trained from [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on [RLHFlow/Mistral-PRM-Data](https://huggingface.co/datasets/RLHFlow/Mistral-PRM-Data) for 1 epochs. We use a global batch size of 32 and a learning rate of 2e-6, where we pack the samples and split them into chunks of 8192 token. See more training details at https://github.com/RLHFlow/Online-RLHF/blob/main/math/llama-3.1-prm.yaml .
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## BoN evaluation result for Mistral generator:
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| Model | Method | GSM8K | MATH |
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| ------------- | ------------- | ------------- | -------- |
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| Mistral-7B | Pass@1 | 77.9 | 28.4 |
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| Mistral-7B | Majority Voting@1024 | 84.2 | 36.8 |
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| Mistral-7B | Mistral-ORM@1024 | 90.1 | 43.6 |
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| Mistral-7B | Mistral-PRM@1024 | 92.4 | 46.3 |
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## Scaling the inference sampling to N=1024 for Deepseek generator:
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| Model | Method | GSM8K | MATH |
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| ------------- | ------------- | ------------- | -------- |
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| Deepseek-7B | Pass@1 | 83.9 | 38.4 |
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| Deepseek-7B | Majority Voting@1024 | 89.7 | 57.4 |
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| Deepseek-7B | Deepseek-ORM@1024 | 93.4 | 52.4 |
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| Deepseek-7B | Deepseek-PRM@1024 | 93.0 | 58.1 |
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| Deepseek-7B | Mistral-ORM@1024 (OOD) | 90.3 | 54.9 |
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| Deepseek-7B | Mistral-PRM@1024 (OOD) | 91.9 | 56.9 |
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## Visualization
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## Usage
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See https://github.com/RLHFlow/RLHF-Reward-Modeling/tree/main/math-rm for detailed examples.
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## Citation
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The automatic annotation was proposed in the Math-shepherd paper:
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```
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@inproceedings{wang2024math,
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title={Math-shepherd: Verify and reinforce llms step-by-step without human annotations},
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author={Wang, Peiyi and Li, Lei and Shao, Zhihong and Xu, Runxin and Dai, Damai and Li, Yifei and Chen, Deli and Wu, Yu and Sui, Zhifang},
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booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
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pages={9426--9439},
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year={2024}
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}
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```
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If you find the training recipe useful, please consider cite it as follows.
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```
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@misc{xiong2024rlhflowmath,
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author={Wei Xiong and Hanning Zhang and Nan Jiang and Tong Zhang},
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title = {An Implementation of Generative PRM},
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year = {2024},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/RLHFlow/RLHF-Reward-Modeling}}
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}
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```
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