Research Paper#Reinforcement Learning, Human Feedback, Preference Learning🔬 ResearchAnalyzed: Jan 3, 2026 06:14
ResponseRank: Learning Preference Strength for RLHF
Analysis
This paper introduces ResponseRank, a novel method to improve the efficiency and robustness of Reinforcement Learning from Human Feedback (RLHF). It addresses the limitations of binary preference feedback by inferring preference strength from noisy signals like response times and annotator agreement. The core contribution is a method that leverages relative differences in these signals to rank responses, leading to more effective reward modeling and improved performance in various tasks. The paper's focus on data efficiency and robustness is particularly relevant in the context of training large language models.
Key Takeaways
- •Proposes ResponseRank, a method for learning preference strength from noisy signals in RLHF.
- •Uses relative differences in proxy signals (response times, annotator agreement) to rank responses.
- •Demonstrates improved sample efficiency and robustness across synthetic, language modeling, and RL control tasks.
- •Introduces the Pearson Distance Correlation (PDC) metric for evaluating utility learning.
Reference
“ResponseRank robustly learns preference strength by leveraging locally valid relative strength signals.”