CHASE-VLA: Post-Training Quantization Framework for Vision-Language-Action Models with Chunk-Aware Scale Estimation
cs.CV
Submitted: 2026-10-02
Updated: 2026-10-02
Terminology
Sources
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- RT-1: Robotics Transformer for Real-World Control at Scale
- UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
- Octo: An Open-Source Generalist Robot Policy
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
- OpenVLA: An Open-Source Vision-Language-Action Model
- CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation
- BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation
- DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control
- DA-PTQ: Drift-Aware Post-Training Quantization for Efficient Vision-Language-Action Models
- QVLA: Not All Channels Are Equal in Vision-Language-Action Model's Quantization
- DyQ-VLA: Temporal-Dynamic-Aware Quantization for Embodied Vision-Language-Action Models
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