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arXiv · 2609.34036

UOPD: Uncertainty-Aware Intervention for On-Policy Distillation of Multi-Turn Agents

Wenbo Zhang·Pengcheng Xu·Weizhi Du·Jing Zhang·Hengrui Cai
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Abstract

On-policy distillation (OPD) trains a student on its own rollouts using dense supervision from a teacher. In multi-turn environments, a mistake at a critical decision step can redirect the subsequent rollout toward poor outcomes. We use low teacher confidence on student actions to select high-uncertainty steps for correction. In a controlled ALFWorld study, a single teacher correction at a low-confidence step improves subsequent student behavior and task success, motivating selective intervention during distillation. We propose UOPD, an uncertainty-aware intervention method for on-policy distillation. At low-uncertainty turns, UOPD executes student actions and applies the standard OPD loss. At high-uncertainty turns, it samples and executes teacher actions and trains the student to imitate them through supervised fine-tuning, which minimizes forward Kullback-Leibler divergence in expectation. UOPD utilizes adaptive uncertainty thresholds to target a scheduled intervention rate. Empirically, we evaluate UOPD across a broad range of agentic tasks, including ALFWorld, WebShop, and Search, demonstrating its superior performance over OPD methods and their variants. UOPD improves WebShop score by up to $15.8\%$ relative to standard OPD.

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Wenbo Zhang, Pengcheng Xu, Weizhi Du, Jing Zhang, Hengrui Cai. 2026-09-27. UOPD: Uncertainty-Aware Intervention for On-Policy Distillation of Multi-Turn Agents. https://arxiv.org/abs/2609.34036

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