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

Key Takeaway

VLMs can be more useful as selective training guides than as direct policies—by querying them only when needed and validating their advice through environment rewards, you can build cheaper, better-performing autonomous agents that don't depend on the VLM at deployment.