Staircase Policy: Streaming Inference for World-Action Models with Large Action Chunks
STAIRCASE POLICY is introduced, a streaming inference and training framework that turns a flow-matching VLA into a JEPA-style WAM and partitions a large action chunk into sub-chunks at staggered denoising stages, enabling long-horizon execution without repeated full policy inference.
Guoheng Sun, Chen Chen, Jin Wang et al.
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