Frontier-OPD：发现智能体能力边界并教智能体超越边界

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Abstract:On-policy distillation aligns teacher feedback with a student's current behavior, yet acquiring useful supervision for multi-turn agents requires identifying both where guidance is needed and whether the student can sustain the interaction afterward. We introduce Frontier-OPD, which addresses this problem through the student's learning frontier: decision situations that remain challenging but admit useful autonomous continuation after brief teacher guidance. To operationalize this frontier, Frontier-OPD organizes on-policy experience into a shared-state trajectory graph, where trajectory-balanced aggregation of teacher-student disagreement identifies distinct intervention candidates while preserving the histories needed for replay. Short teacher bridges are then assessed by comparing agreement over the bridge and the ensuing student continuation with an equally long segment of the original trajectory, providing a behavioral proxy for guidance that the current student can build on. Accepted teacher responses augment ordinary distillation data, and fresh rollouts renew the frontier estimate as the policy changes. Experiments on ALFWorld, WebShop, and ScienceWorld report higher mean autonomous performance than the compared distillation baselines across the evaluated student scales and teacher configurations, with ablations indicating complementary effects of candidate selection and guidance filtering. In one ALFWorld timing setting, the additional collection cost is offset by a shorter time to a target success rate.

Subjects:	Computation and Language (cs.CL)
Cite as:	arXiv:2610.32802 [cs.CL]
	(or arXiv:2610.32802v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2610.32802 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zheng Jiang [view email]
[v1] Fri, 23 Oct 2026 15:40:44 UTC (2,326 KB)