Cover Image

HBVLA: Pushing 1-Bit Post-Training Quantization for Vision-Language-Action Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yan, Xin, Wan, Zhenglin, Ye, Feiyang, Yu, Xingrui, Du, Hangyu, You, Yang, Tsang, Ivor
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Backdoor-Based Ownership Verification for Vision-Language-Action Models
by: Sun, Ming, et al.
Published: (2026)

Time-Annealed Perturbation Sampling: Diverse Generation for Diffusion Language Models
by: Wu, Jingxuan, et al.
Published: (2026)

Letting Trajectories Spread: Quality-Preserving Control for Diverse Flow Matching
by: Wu, Jingxuan, et al.
Published: (2025)

SA-VLA: Spatially-Aware Flow-Matching for Vision-Language-Action Reinforcement Learning
by: Pan, Xu, et al.
Published: (2026)

FM-IRL: Flow-Matching for Reward Modeling and Policy Regularization in Reinforcement Learning
by: Wan, Zhenglin, et al.
Published: (2025)

CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMs
by: Wang, Haoyu, et al.
Published: (2024)

Adversarial Dual On-Policy Distillation from Expressive Teacher
by: Wan, Zhenglin, et al.
Published: (2026)

Advancing Analytic Class-Incremental Learning through Vision-Language Calibration
by: Zhao, Binyu, et al.
Published: (2026)

Imitation from Diverse Behaviors: Wasserstein Quality Diversity Imitation Learning with Single-Step Archive Exploration
by: Yu, Xingrui, et al.
Published: (2024)

Evolving Diffusion and Flow Matching Policies for Online Reinforcement Learning
by: Zhang, Chubin, et al.
Published: (2025)

QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
by: Zhang, Jingxuan, et al.
Published: (2026)

PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models
by: Zhao, Jiaqi, et al.
Published: (2025)

1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit
by: Gao, Chang, et al.
Published: (2024)

Bi-VLM: Pushing Ultra-Low Precision Post-Training Quantization Boundaries in Vision-Language Models
by: Wang, Xijun, et al.
Published: (2025)

Diversifying Policy Behaviors with Extrinsic Behavioral Curiosity
by: Wan, Zhenglin, et al.
Published: (2024)

A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization
by: Ye, Feiyang, et al.
Published: (2024)

Dataset Color Quantization: A Training-Oriented Framework for Dataset-Level Compression
by: Yu, Chenyue, et al.
Published: (2026)

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization
by: Bai, Runsheng, et al.
Published: (2024)

Quant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models
by: Zhang, Tianao, et al.
Published: (2025)

Pushing the Limits of Block Rotations in Post-Training Quantization
by: Sanjeet, Sai, et al.
Published: (2026)

ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training
by: Yang, Rushuai, et al.
Published: (2026)

DA-PTQ: Drift-Aware Post-Training Quantization for Efficient Vision-Language-Action Models
by: Xu, Siyuan, et al.
Published: (2026)

Reflection-Driven Control for Trustworthy Code Agents
by: Wang, Bin, et al.
Published: (2025)

Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
by: Deng, Jinliang, et al.
Published: (2024)

Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs
by: Aggarwal, Shivam, et al.
Published: (2023)

Sharpness-Aware Black-Box Optimization
by: Ye, Feiyang, et al.
Published: (2024)

BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
by: Huang, Wei, et al.
Published: (2024)

Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit Post-Training Quantization in Vision Transformers
by: Ding, Rui, et al.
Published: (2024)

AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers
by: Jiang, Runqing, et al.
Published: (2025)

Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration
by: Zhao, Heyang, et al.
Published: (2025)

Interactive Post-Training for Vision-Language-Action Models
by: Tan, Shuhan, et al.
Published: (2025)

UniErase: Towards Balanced and Precise Unlearning in Language Models
by: Yu, Miao, et al.
Published: (2025)

Numerical Sensitivity and Robustness: Exploring the Flaws of Mathematical Reasoning in Large Language Models
by: Sun, Zhishen, et al.
Published: (2025)

Plug-and-Play 1.x-Bit KV Cache Quantization for Video Large Language Models
by: Tao, Keda, et al.
Published: (2025)

Fine-Grained Post-Training Quantization for Large Vision Language Models with Quantization-Aware Integrated Gradients
by: Xiang, Ziwei, et al.
Published: (2026)

Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference
by: Zhang, Chenxiang, et al.
Published: (2025)

BitSnap: Checkpoint Sparsification and Quantization in LLM Training
by: Peng, Yanxin, et al.
Published: (2025)

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models
by: Ding, Xin, et al.
Published: (2025)

Scaling Laws for Post Training Quantized Large Language Models
by: Xu, Zifei, et al.
Published: (2024)

Post Training Quantization of Large Language Models with Microscaling Formats
by: Sharify, Sayeh, et al.
Published: (2024)