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README.md
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To train this model, we used a 70B RM trained on the UltraFeedback data, and then used a mixture of prompts during PPO training.
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For more details, read the paper:
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[Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback](https://
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## .Model description
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title={{Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback}},
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author={{Hamish Ivison and Yizhong Wang and Jiacheng Liu and Ellen Wu and Valentina Pyatkin and Nathan Lambert and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi}}
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year={2024},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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To train this model, we used a 70B RM trained on the UltraFeedback data, and then used a mixture of prompts during PPO training.
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For more details, read the paper:
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[Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback](https://arxiv.org/abs/2406.09279).
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## .Model description
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title={{Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback}},
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author={{Hamish Ivison and Yizhong Wang and Jiacheng Liu and Ellen Wu and Valentina Pyatkin and Nathan Lambert and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi}}
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year={2024},
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eprint={2406.09279},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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