PearlVLA：在潜在空间中渐进式精炼具身动作规划

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Abstract:Current Vision-Language-Action (VLA) models face a trade-off between efficient action generation and explicit deliberation. Directly decoding actions from vision-language backbone representations enables low-latency control, whereas textual reasoning, pixel-level subgoals, or world-model evaluation of decoded actions can improve planning but incur substantial latency and computational cost. We propose PearlVLA, a VLA framework that progressively refines a VLM-derived latent plan using feedback from the predicted consequence of each intermediate plan. PearlVLA uses a frozen latent world model (LaWM) pretrained on action-free video. At each refinement round, the current plan produces a continuous latent action code, and the LaWM predicts the corresponding latent visual subgoal. A future-guided plan refiner uses this subgoal to update the plan, so each revision reshapes the next LaWM query. After K rounds, the refined plan is passed once to the host policy's action interface to produce an action chunk. We further introduce Causal Refinement-Grouped Process-Reward RL to optimize latent refinement by comparing rewards from the longer-horizon imagined futures of plan edits made at the same refinement state. Experiments on the LIBERO and RoboCasa benchmarks show that PearlVLA performs competitively against strong existing methods.

Submission history

From: Bochen Yang [view email]
[v1] Tue, 16 Jun 2026 13:38:03 UTC (1,492 KB)
[v2] Thu, 8 Oct 2026 14:24:06 UTC (2,514 KB)