arXiv:2512.06343v3 Announce Type: replace-cross Abstract: Reward models are central to Large Language Model (LLM) alignment within the framework of RLHF. The standard objective used in reward modeling is the Bradley-Terry (BT) loss, which learns from pairwise data consisting of chosen and rejected responses. In this work, we analyze the per-sample gradient of BT-loss and show spurious learning signals due to representation distance. In particular, BT gradient norm scales with two distinct components: (1) prediction error, reflected by the difference in predicted rewards between chosen and reje
This paper offers a technical analysis of a core component of LLM alignment, addressing specific challenges in scaling and improving reward models, which are central to current AI development paradigms.
Understanding and addressing biases in reward models directly impacts the safety, effectiveness, and future development trajectory of large language models, a foundational technology for many emerging AI applications.
Improved understanding of the 'representation distance bias' in BT-loss for reward models could lead to more robust and reliable LLMs, potentially accelerating their deployment in sensitive applications.
- · AI researchers
- · LLM developers
- · AI safety organizations
- · Developers of flawed Reward Models
- · Users of biased LLMs
Further research and development of more robust reward modeling techniques are likely.
Improved model alignment could accelerate the deployment of LLMs in critical commercial and defense applications.
More reliable AI systems could reduce regulatory friction and increase public trust in advanced AI, influencing broader adoption.
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Read at arXiv cs.CL