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The paper introduces Retrieve in Time, Correct in Frequency (RTCF), a test-time correction framework designed to enhance the performance of frozen vision-language-action (VLA) policies during long-horizon manipulation tasks. By employing Progressive Memory Alignment (PMA), RTCF effectively aligns visual execution history with successful trajectories, allowing for the transfer of low-frequency residuals while retaining high-frequency components from the original policy. The method demonstrates a significant improvement in success rates across multiple LIBERO suites, raising aggregate success from 86.4% to 88.4% and LIBERO-Long from 61.6% to 68.6%, all without requiring parameter updates or additional computational resources.
RTCF boosts the success of frozen VLA policies by leveraging past experiences without the need for retraining or extra GPU power.
Frozen vision-language-action (VLA) policies generate temporally extended action chunks, but long-horizon manipulation remains vulnerable to accumulated execution error and visual aliasing across task stages. Successful rollouts provide useful corrective evidence, yet current frame retrieval can return progress-misaligned actions,while direct replay or time-domain fusion can overwrite the reactive structure of the policy proposal. We introduce Retrieve in Time, Correct in Frequency (RTCF), a training-free test-time correction framework that improves frozen VLA performance with low model-side overhead.RTCF separates which experience to retrieve from which part of its action to transfer. Progressive Memory Alignment (PMA) causally aligns the growing visual execution history with complete successful trajectories through incrementally updated monotonic frontiers, jointly identifying a relevant memory and the current aligned memory position without stage labels. From the aligned action chunk,RTCF transfers a coefficient-wise-clipped low-frequency residual on motion channels. Higher-frequency components and gripper decisions remain inherited from the frozen policy. Across four LIBERO suites and 2,000 episodes per condition, RTCF raises aggregate success from 86.4% to 88.4% and improves LIBERO-Long from 61.6% to 68.6%.These gains require no parameter updates, repeated VLA inference, or additional GPU resources: correction can be performed on the client CPU after a single policy invocation, and the median latencies sum to only 10.99 ms per action chunk
Tsinghua AI