StepOPSD: Step-Aware Online Preference Distillation for Agent Reinforcement Learning

Reinforcement learning for multi-turn agents suffers from a credit-assignment mismatch: rewards are sparse and trajectory-level, while success often hinges on a few local decisions. Existing online policy distillation (OPD) provides denser token-level supervision, but typically treats heterogeneous agent trajectories as monolithic strings rather than causal interaction units. We present StepOPSD, a post-rollout preference self-distillation framework that takes the agent step as the unit of credi

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ethics.ai (26 May 2026), “StepOPSD: Step-Aware Online Preference Distillation for Agent Reinforcement Learning,” evidence record 3668, https://ethics.ai/record/3668 (originally published by arXiv).

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