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CoRT: Counterfactual Replay for Token-Level Rubric-Guided Policy Optimization

73 upvotes · 28 JUL 2026 · Bo-Wen Zhang, Junwei He, Wen Wang et al.

This paper proposes a method to improve language model training by allocating credit to individual tokens within a response, allowing for more nuanced evaluation of model performance. Practitioners may care about this method as it can lead to better language model performance, especially in tasks that require specific formatting or semantic choices.

rubric-guided reinforcement learningcontinual learningtoken-level credit weightingcounterfactual replaypolicy optimization