cs.AIOct 15, 2026

CleanRM: Reward Modeling from Noisy Feedback with a Few Clean Feedback for RLHF

Authors: Licheng Pan, Haocheng Yang, Yuzhou Chen, Yichen Sun, Yuan Lu, Yunfei Teng, Jiajun Fan, Guangyi Liu, 

Organizations: Zhejiang University · Xiaohongshu Inc · National University of Singapore · New York University · 

Abstract

Feedback noise is pervasive in preference data, arising from subjective human judgments, annotation mistakes, and imperfect LLM judges. Standard reward modeling trained on such feedback yields a biased estimate of the clean risk. Although classical denoising methods are directly applicable to noisy feedback learning, they generally assume a uniform noise ratio across samples. However, this assumption fails for feedback data, where noise is intrinsically sample-wise. To address this challenge, we propose CleanRM, which uses a few clean feedback to calibrate sample-wise noise ratios via optimal transport. Specifically, CleanRM aligns the joint distribution of preferences reverted from noisy feedback with that of clean feedback and then trains the reward model using a noise-corrected surrogate loss. Theoretically, we prove that the resulting objective is unbiased with respect to the clean risk. Extensive experiments demonstrate that CleanRM outperforms state-of-the-art denoising baselines and yields more effective downstream RLHF alignment.

Figures & tables

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Explore similar work

CardsList
  1. Optimal Transport for LLM Reward Modeling from Noisy Preference

    May 7, 2026Licheng Pan, Haochen Yang, Haoxuan Li +8Reward ModelingNoisy-Label Learning

  2. Two Minds Better Than One: Collaborative Reward Modeling for LLM Alignment

    May 15, 2025Jiazheng Zhang, Wenqing Jing, Zizhuo Zhang +12Reward ModelingLLM Alignment

  3. CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks

    Mar 19, 2026Hao Wang, Licheng Pan, Zhichao Chen +7Reward ModelingRL from Human Feedback