--- library_name: transformers tags: [] --- This is the SFT checkpoint used for the project [RLHFlow/Online-RLHF](https://github.com/RLHFlow/Online-RLHF) * **Paper**: [RLHF Workflow: From Reward Modeling to Online RLHF](https://arxiv.org/pdf/2405.07863) (Published in TMLR, 2024) * **Authors**: Hanze Dong*, Wei Xiong*, Bo Pang*, Haoxiang Wang*, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang * **Code**: https://github.com/RLHFlow/Online-RLHF The model is trained from [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on a mixture of diverse open-source high-quality data for 1 epoch with detailed parameters in the report. It has not been trained by RLHF and can serve as a good starting point for the RLHF research. ## Academic Benchmarks We use ToRA script to evaluate GSM8K and MATH, Evalplut for HumanEval, and lm-evaluation-harness for other benchmarks. The model is evaluated in zero-shot setting so the results here may be slightly different from that reported in the technical report. | **Model** | **Size** | **Method** | **LC AlpacaEval** | **MT-Bench** | **GSM-8K** | **MMLU** | **HumanEval** | **TruthfulQA** | **ARC** | **MBPP** | |----------------------------|----------|-----------------|------------|------------|------------|----------|---------------|----------------|---------|----------| | LLaMA-3-8B-it | 8B | RS+DPO+PPO |22.9|8.16| 79.6 | 66.0 | 61.6 | 43.9 | 59.5 | 61.1 | | Ours (SFT baseline) | 8B | SFT |10.2|7.69| 74.2 | 30.0 | 64.6 | 63.4 | 53.5 | 58.6 | | Ours (Iterative RLHF) | 8B | Iterative DPO |37.2|8.46| 80.7 | 65.3 | 64.6 | 60.4 | 64.3 | 60.8 | ## Citation Please cite our techical report if you find our model is useful for your research or product. ``` @misc{dong2024rlhf, title={RLHF Workflow: From Reward Modeling to Online RLHF}, author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang}, year={2024}, eprint={2405.07863}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```