RM-R1: Reward Modeling as Reasoning
Xiusi Chen, Gaotang Li, Ziqi Wang, Bowen Jin, Cheng Qian, Yu Wang, Hongru Wang, Yu Zhang, Denghui Zhang, Tong Zhang, Hanghang Tong, Heng Ji
2025-05-06
Summary
This paper talks about RM-R1, a new approach that helps AI systems figure out what counts as a good answer by teaching them to reason through their decisions, not just guess based on patterns.
What's the problem?
Traditional reward models in AI often just look for surface-level clues to decide if an answer is good, which can lead to mistakes or answers that don't really make sense.
What's the solution?
The researchers improved these models by adding reasoning tasks, so the AI learns to think through problems step by step, making its decisions easier to understand and more accurate.
Why it matters?
This matters because it helps AI give better, more trustworthy answers, which is important for everything from homework help to making safe decisions in real-world situations.
Abstract
Reasoning Reward Models (ReasRMs) enhance reward modeling for large language models by integrating reasoning tasks, improving interpretability and performance.