Selective Agent Guidance via Entropy: Learning Autonomous Policies from Imperfect VLM Teachers
[Sztuczna inteligencja] Autorzy: Matteo Merler, Giovanni Bonetta, Davide Zago i 2 więcej
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 (Selective Agent Guidance via Entropy), a framework that queries a VLM only when the learner is uncertain, executes the suggested action during training, and distills guidance into a lightweight Reinforcement Learning (RL) policy. Because VLM advice is not always reliable, SAGE can weight teacher-action distillation using environment-derived advantages rather than treating all suggestions as equally useful. Across sparse-reward visual reasoning and navigation tasks, SAGE learns policies that act wi…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja