Temporally Coherent Reward Modeling adds Monte Carlo and TD regularizers to Bradley-Terry training so reward model outputs at every token become conditional expectations of the final reward, improving token-level interpretability, process supervision from outcome data, and PPO efficiency.
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Reward Models Are Secretly Value Functions: Temporally Coherent Reward Modeling
Temporally Coherent Reward Modeling adds Monte Carlo and TD regularizers to Bradley-Terry training so reward model outputs at every token become conditional expectations of the final reward, improving token-level interpretability, process supervision from outcome data, and PPO efficiency.