Recovering Low-Bit Failures in Vision-Language-Action Policies via Quantization-Conditioned Reinforcement Learning

arXiv:2610.15675 · cs.RO, cs.CV · Submitted 2026-10-11 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-10-11

Updated: 2026-10-11

Code: https://github.com/apple-aiml-research/ml-stable-diffusion

Terminology

• Quantization — 1× in this paper · explained in 12 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• A White Paper on Neural Network Quantization
• SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
• QVLA: Not All Channels Are Equal in Vision-Language-Action Model's Quantization
• Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
• Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
• $\pi^{*}_{0.6}$: a VLA That Learns From Experience

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