LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling
Authors: name: Zecheng Tang, name: Baibei Ji, name: Quantong Qiu, name: Haitian Wang, name: Xiaobo Liang, name: Juntao Li, name: Min Zhang
Organization: Soochow University

Reward model (RM) plays a pivotal role in aligning large language model (LLM)
with human preferences. As real-world applications increasingly involve long
history trajectories, e.g., LLM agent, it becomes indispensable to evaluate
whether a model's responses are not only high-quality but also grounded in and
consistent with the provided context. Yet, current RMs remain confined to
short-context settings and primarily focus on response-level attributes (e.g.,
safety or helpfulness), while largely neglecting the critical dimension of long
context-response consistency. In this work, we introduce Long-RewardBench, a
benchmark specifically designed for long-context RM evaluation, featuring both
Pairwise Comparison and Best-of-N tasks. Our preliminary study reveals that
even state-of-the-art generative RMs exhibit significant fragility in
long-context scenarios, failing to maintain context-aware preference judgments.
Motivated by the analysis of failure patterns observed in model outputs, we
propose a general multi-stage training strategy that effectively scales
arbitrary models into robust Long-context RMs (LongRMs). Experiments show that
our approach not only substantially improves performance on long-context
evaluation but also preserves strong short-context capability. Notably, our 8B
LongRM outperforms much larger 70B-scale baselines and matches the performance
of the proprietary Gemini 2.5 Pro model.