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arXiv Computation and Language By Wenxi Gan

Learning to Act with Task Progress: Distilling Small Agents from Compact Teacher Supervision

Read the original on arXiv Computation and Language →

The paper introduces Task-Progress Distillation (TPD), an offline method that pairs each action in teacher demonstrations with a short label indicating the current task stage. A small 1.7B student model trained with 404 demonstrations using TPD achieves 72.4% mean unseen task success on ALFWorld, matching action-only supervision and outperforming a reasoning-trained student. At lower demonstration budgets, explicit stage labels significantly boost performance, raising success from 48.0% to 67.7% with 200 demos, and both approaches converge to 76.9% with 808 demos.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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