A constrained optimization layer interleaved with flow-matching denoising enables predictive collision avoidance in VLA models, delivering 82.8% collision avoidance and 81.6% task success on SafeLIBERO with gains over single-step baselines.
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A 0.88B-parameter VLA reaches 90.0% MT50 success, and a single-query conformal head returns a calibrated joint uncertainty set over the executed action prefix.
Z-1 uses task-wise GRPO post-training on a flow-based VLA model to reach 80.6% average success across 24 RoboCasa tasks, a 13.2-point gain over its SFT baseline.
COAST applies contrastive conceptors to steer VLA hidden states into task-specific success subspaces, yielding over 20% simulation and 40% real-robot success rate gains across three distinct policies.
citing papers explorer
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Neuro-Symbolic Safety Guidance for Vision-Language-Action Models via Constrained Flow Matching
A constrained optimization layer interleaved with flow-matching denoising enables predictive collision avoidance in VLA models, delivering 82.8% collision avoidance and 81.6% task success on SafeLIBERO with gains over single-step baselines.
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FabriVLA: A Lightweight Vision-Language-Action Model with Conformal Action Chunk Uncertainty
A 0.88B-parameter VLA reaches 90.0% MT50 success, and a single-query conformal head returns a calibrated joint uncertainty set over the executed action prefix.
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Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models
Z-1 uses task-wise GRPO post-training on a flow-based VLA model to reach 80.6% average success across 24 RoboCasa tasks, a 13.2-point gain over its SFT baseline.
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Contrastive Conceptor Activation Steering (COAST): Unlocking Vision-Language-Action Models through Hidden States
COAST applies contrastive conceptors to steer VLA hidden states into task-specific success subspaces, yielding over 20% simulation and 40% real-robot success rate gains across three distinct policies.