17 citations · 19 across the 4 of their papers we have counts for
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cs.CL2024
RMB: Comprehensively Benchmarking Reward Models in LLM Alignment
Enyu Zhou, Guodong Zheng, Binghai Wang +11
Reward models (RMs) guide the alignment of large language models (LLMs), steering them toward behaviors preferred by humans. Evaluating RMs is the key to better aligning LLMs. Howe…
cs.LG2024★ 1 cited
InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling
Yuchun Miao, Sen Zhang, Liang Ding +3
Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models with human values, reward hacking, also termed reward overoptimization, remains…