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RLHFlow/
Llama3.1-8B-PRM-Deepseek-Data

Name: Llama3.1-8B-PRM-Deepseek-Data
Availability: InStock

text-generation-inference

endpoints_compatible

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This is a process-supervised reward (PRM) from the project RLHFlow/RLHF-Reward-Modeling

The model is trained from meta-llama/Llama-3.1-8B-Instruct on RLHFlow/Deepseek-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/RLHF-Reward-Modeling/tree/main/math-rm.

BoN evaluation result for Mistral generator:

Model	Method	GSM8K	MATH
Mistral-7B	Pass@1	77.9	28.4
Mistral-7B	Majority Voting@1024	84.2	36.8
Mistral-7B	Mistral-ORM@1024	90.1	43.6
Mistral-7B	Mistral-PRM@1024	92.4	46.3

Scaling the inference sampling to N=1024 for Deepseek generator:

Model	Method	GSM8K	MATH
Deepseek-7B	Pass@1	83.9	38.4
Deepseek-7B	Majority Voting@1024	89.7	57.4
Deepseek-7B	Deepseek-ORM@1024	93.4	52.4
Deepseek-7B	Deepseek-PRM@1024	93.0	58.1
Deepseek-7B	Mistral-ORM@1024 (OOD)	90.3	54.9
Deepseek-7B	Mistral-PRM@1024 (OOD)	91.9	56.9

Visualization

Usage

See https://github.com/RLHFlow/RLHF-Reward-Modeling/tree/main/math-rm for detailed examples.

Citation

The automatic annotation was proposed in the Math-shepherd paper:

@inproceedings{wang2024math,
  title={Math-shepherd: Verify and reinforce llms step-by-step without human annotations},
  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},
  booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={9426--9439},
  year={2024}
}

If you find the training recipe useful, please consider cite it as follows.

@misc{xiong2024implementation,
  title={An implementation of generative prm},
  author={Xiong, Wei and Zhang, Hanning and Jiang, Nan and Zhang, Tong},
  year={2024}
}

RLHFlow/Llama3.1-8B-PRM-Deepseek-Data