slow-chunk-proposal-plus-fast-rl-trained-action TEC-0208 · proposedSlow chunk proposal plus fast RL-trained action edit
A large VLA asynchronously samples candidate action chunks from an older observation, then a lightweight edit policy conditioned on the latest observation perturbs each candidate and a learned Q-function picks the best chunk to execute.
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
A large VLA asynchronously samples candidate action chunks from an older observation, then a lightweight edit policy conditioned on the latest observation perturbs each candidate and a learned Q-function picks the best chunk to execute. Filed by the drafting stage from Reinforcement Learning for Real-Time Vision-Language-Action Policies (2609.18207) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Success rate against RLPD, DSRL, DSRL+RTC, EXPO-FT, EXPO-FT+RTC and SFT baselines: best in 10/10 Kinetix environments (96.2% average under 4-step delay, above the 81.4% zero-delay RLPD baseline) and 29/30 average over four real tasks versus 25/30 for the strongest baseline, plus delay and speed sweeps.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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