Online-DPO-R1

• Blog: https://www.notion.so/Online-DPO-R1-1908b9a70e7b80c3bc83f4cf04b2f175
• Authors:
• Code: https://github.com/RLHFlow/Online-DPO-R1

Introduction

Model Releases

• [PPO model] (https://huggingface.co/RLHFlow/Qwen2.5-7B-PPO-Zero)
• [Iterative DPO from SFT model] (https://huggingface.co/RLHFlow/Qwen2.5-7B-DPO)
• [Iterative DPO from base model] (https://huggingface.co/RLHFlow/Qwen2.5-7B-DPO-Zero)
• [Iterative DPO with Negative Log-Likelihood (NLL)] (https://huggingface.co/RLHFlow/Qwen2.5-7B-DPO-NLL-Zero)
• [Raft] (https://huggingface.co/RLHFlow/Qwen2.5-7B-RAFT-Zero)

Dataset

Training methods

• Iterative DPO: Following the RLHF Workflow framework (https://arxiv.org/pdf/2405.07863), in each iteration, we sample multiple responses from the last trained policy, rank them via the ruled-based reward, and construct the preference pairs. Then, we optimize the policy by minimizing the DPO loss and enter the next iteration. Online iterative DPO can mitigate the issue of distribution shift and the limited coverage of offline data effectively

Performance

Usage

Citation