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CoRL 2026 paper proposes language feedback policy that can steer frozen VLAs harmlessly

The paper proposes a framework that interactively searches for language sequences that improve closed-loop VLA task performance, distills them into a test-time language feedback policy (LFP), and trains an improvement head to predict when language steering will be effective, then applies conformal calibration to the improvement head to prevent harmful interventions.

Part of the event: Proposing a Language Feedback Policy that Provides Harmless Guidance for Frozen VLAs

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