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Submitted to ICLR 2027
Critic-Guided Schrödinger Transport for Online VLA Refinement
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Critic-guided Schrödinger bridge refinement adapts frozen vision-language-action (VLA) policies using sparse rewards. A lightweight two-step actor removes candidate search and critic evaluation from inference. Evaluated on LIBERO, LIBERO-PRO, and bimanual robot hardware, with 92.3% mean LIBERO success compared with a 53.1% base policy, and stacking time reduced from 26.1 to 13.3 seconds.






