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PaceVLA: Adaptive Temporal Action Quantization in Vision-Language-Action Models

PaceVLA proposes an adaptive temporal execution framework for VLAs that uses multi-granularity action decoders on a shared action expert, along with quantizability-guided mode selection and context-conditioned routing, to decide at each inference step whether to adopt coarse-grained execution. Experiments cover three multi-task simulation benchmarks and six real-world tasks, improving execution efficiency while maintaining task success rates across multiple embodiments, including long-horizon and dexterous manipulation.

Part of the event: PaceVLA proposes an adaptive temporal action quantization framework for VLA

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