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

Rubric-based reinforcement learning enriches language model training by evaluating model outputs against explicit criteria. Yet in GRPO-style pipelines, these structured judgments are reduced to a scalar response-level reward and converted into a response-level advantage, which is broadcast uniformly to all generated tokens. This leaves no explicit mechanism for allocating credit within a response, even when different criteria are grounded in different spans, formatting decisions, or semantic ch

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ethics.ai (27 July 2026), “CoRT: Counterfactual Replay for Token-Level Rubric-Guided Policy Optimization,” evidence record 14530, https://ethics.ai/record/14530 (originally published by HuggingFace Daily Papers).

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