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cs.LG2026
BiasReducer: Adaptive Bias Mitigation for Reward Models
Shuang Liu, Yongliang Miao, Yanguang Liu +2
Reward models score responses from large language models (LLMs) and guide LLM training toward human preferences. However, reward models can favor superficial attributes such as len…
cs.LG2026
HARVE: Hacking-Aware Reward-Head Vector Editing for Robust Reward Models
Shuang Liu, Yuxuan Bo, Qiuyang Zhao +4
Reward models are central to large language model (LLM) alignment, but they remain vulnerable to reward hacking. To evaluate reward-model robustness, we introduce RewardHackBench c…