Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint
Paper · 2023 · Wei Xiong, Hanze Dong, Chenlu Ye et al.
Summary
Popular RLHF methods such as offline PPO and offline DPO learn from one fixed preference dataset. To reach the optimal policy, that dataset would need to cover almost the entire prompt–response space, which is unrealistic given how vast the space of possible responses is. The authors cast alignment as a reverse-KL regularized contextual bandit. In this setup the best policy stays stochastic and close to the initial checkpoint. They provide finite-sample guarantees for offline, online and hybrid regimes. Offline, pessimistic (conservative) reward estimation is provably sample-efficient. Online and hybrid, batch exploration with fresh preference feedback provably helps. The resulting framework, GSHF, sits on top of existing optimizers (DPO, PPO, InfoNCA) and yields two practical algorithms: iterative online DPO that explores via rejection sampling, and multi-step rejection sampling for offline data. In LLM alignment experiments, both outperform DPO and RSO. The practical lesson is to keep collecting preference labels on outputs from the current policy rather than relying on a single static dataset.
Key points
- Small-scale study: used Open-LLaMA-3B-V2 for controlled simulation runs.
- Scaled-up setup: started from Zephyr-SFT-beta (Mistral-7B-v0.1 fine-tuned on about 200K UltraChat samples). Training used UltraFeedback prompts and UltraRM-13B as the preference signal, with best-of-8 rejection sampling for exploration and three Online-GSHF-DPO iterations of two DPO epochs each.
- Evaluation: AlpacaEval-2 (805 prompts, judged against GPT-4 Preview 11/06). The length-controlled win rate reached 24.17% after three iterations (raw win rate 30.49%), surpassing offline DPO (Zephyr-Beta) and RAFT. A stronger reward model plus a filtered and augmented prompt set raised it to 34.79% (model FsfairX-Zephyr-Chat-v0.1).
- Length growth: Hybrid-GSHF-DPO output length rose from 161 to 243 to 263 tokens across iterations. For comparison, DPO, RSO and Multi-step RSO averaged 241, 275 and 240, which suggests some reward gain comes from verbosity.
- No external responses: no responses from closed-source LLMs were used for training. The only external signal was relative preference.
Institutions
Fields, topics and concepts
📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksDecision Sciences · 249 worksManagement Science and Operations Research · 134 works
Cite
Cite as Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang and Tong Zhang. 2023. Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint. arXiv:2312.11456.
@misc{xiong2023iterative,
title = {{Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint}},
author = {Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang and Tong Zhang},
year = {2023},
doi = {10.48550/arxiv.2312.11456},
eprint = {2312.11456},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2312.11456}
}References
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