Learning to Act with Task Progress: Distilling Small Agents from Compact Teacher Supervision
Paper recorded by Signals 4 on 2026-10-07 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-07 on arXiv · recorded by Signals 4 on 2026-10-08
Category: cs.CL · 自然语言处理 · first seen 2026-10-08
Abstract
Learning from large-model demonstrations offers a way to train small agents that can complete recurring tasks without calling a large model at every step. A central design choice is what to retain from teacher trajectories that contain reasoning, actions, and information about task progress. We introduce Task-Progress Distillation (TPD), an offline approach that pairs each demonstrated action with
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