Selective Agent Guidance via Entropy: Learning Autonomous Policies from Imperfect VLM Teachers

Paper recorded by Signals 4 on 2026-09-01 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-01 on arXiv · recorded by Signals 4 on 2026-09-02

Category: cs.AI · 人工智能 · first seen 2026-09-02

Abstract

Vision-Language Models (VLMs) provide useful priors for interactive decision-making, but using them directly as policies is expensive and brittle: they must be queried at every step, do not improve from environment interaction, and can repeat systematic errors. We study how to learn a cheap autonomous policy from an online, expensive, and imperfect but informative VLM teacher. We propose SAGE (Sel

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