arXiv: cs.AI preprintsInternational6 November 2026
Using Experience Twice: Dynamic Process Rubrics for Privileged Token-Level Credit Assignment
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arXiv:2611.06543v2 Announce Type: replace Abstract: Agent trajectories often contain both successful decisions and failed attempts, yet optimization with sparse trajectory-level rewards struggles to identify which critical decisions should be reinforced or suppressed. We observe that meaningful distinctions among on-policy trajectories can be distilled into natural-language process rubrics and used as process supervision signals. Based on this insight, we propose Online Rubric Self-Distillation (ORSD), which leverages self-derived process rubrics for dense token-level supervision, thereby augm
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