The emergence of LM-based judging reward modeling, represented by generative reward models, has successfully made reinforcement learning from AI feedback (RLAIF) efficient and scalable. To further advance this paradigm, we propose a core insight: this form of reward modeling shares fundamental formal consistency with natural language inference (NLI), a core task in natural language understanding. This reframed perspective points to a key path for building superior reward models: scaling the model's comprehension boundaries. Pursuing this path, exploratory experiments on NLI tasks demonstrate that the slot prediction masked language models (MLMs) incorporating contextual explanations achieve significantly better performance compared to mainstream autoregressive models. Based on this key finding, we propose ESFP-RM, a two-stage LM-based judging reward model that utilizes an explanation based slot framework for prediction to fully leverage the advantages of MLMs. Extensive experiments demonstrate that in both reinforcement learning from human feedback (RLHF) and out-of-distribution (OOD) scenarios, the ESFP-RM framework delivers more stable and generalizable reward signals compared to generative reward models.
@article{arxiv.2508.18212,
title = {Better Language Model-Based Judging Reward Modeling through Scaling Comprehension Boundaries},
author = {Meiling Ning and Zhongbao Zhang and Junda Ye and Jiabao Guo and Qingyuan Guan},
journal= {arXiv preprint arXiv:2508.18212},
year = {2025}
}
Comments
After further internal discussion, our author team has decided to withdraw this submission due to the need for several important refinements to the manuscript. All co-authors have been informed and agree with this decision