Efficient Process Reward Modeling via Contrastive Mutual Information
Recent research has devoted considerable effort to verifying the intermediate reasoning steps of chain-of-thought (CoT) trajectories using process reward models (PRMs) and other verifier models. However, training a PRM typically requires human annotators to assign reward scores to each reasoning step, which is both costly and time-consuming. Existing automated approaches, such as Monte Carlo (MC) estimation, also demand substantial computational resources due to repeated LLM rollouts. To overcome these limitations, we propose contrastive pointwise mutual information (CPMI), a novel automatic reward labeling method that leverages the model's internal probability to infer step-level supervi...
| Authors | Nakyung Lee, Sangwoo Hong, Jungwoo Lee |
|---|---|
| Primary category | cs.CL |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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