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LIBERO Benchmark
SpecPrune-VLA: Accelerating Vision-Language-Action Models via Action-Aware Self-Speculative Pruning
Published:9/6/2025
Acceleration of Vision-Language-Action ModelsAction-Aware Self-Speculative PruningTraining-Free Pruning MethodsDynamic Layer-Level PruningLIBERO Benchmark
SpecPruneVLA accelerates VisionLanguageAction models by integrating local and global information for efficient pruning. It employs static and dynamic pruning strategies, achieving 1.46x speedup on NVIDIA A800 and 1.57x on RTX 3090, with minimal success rate loss.
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$π_\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
Published:10/30/2025
Flow-based Vision-Language-Action ModelsOnline Reinforcement Learning Fine-TuningLIBERO BenchmarkMultitask Reinforcement LearningDenoising Modeling in Environment Interaction
The paper introduces the framework, using online reinforcement learning to finetune flowbased VisionLanguageAction models, addressing challenges in action loglikelihoods. It demonstrates significant performance improvements on LIBERO and ManiSkill benchmarks.
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