ActionCodec：什么使好的动作分词器成为可能
ActionCodec: What Makes for Good Action Tokenizers
AI总结：
本文提出ActionCodec，通过信息论原则提升动作分词性能，实现无需机器人预训练的VLA模型新SOTA。
摘要：
视觉-语言-动作（VLA）模型利用视觉-语言模型（VLMs）的原生自回归范式，已展现出卓越的指令遵循能力和训练效率。该范式的核心是动作分词，但其设计主要集中在重建保真度上，未能解决其对VLA优化的直接影响。因此，
英文摘要：
Vision-Language-Action (VLA) models leveraging the native autoregressive paradigm of Vision-Language Models (VLMs) have demonstrated superior instruction-following and training efficiency. Central to this paradigm is action tokenization, yet its design has primarily focused on reconstruction fidelity, failing to address its direct impact on VLA optimization. Consequently, the fundamental question of \textit{what makes for good action tokenizers} remains unanswered. In this paper, we bridge this gap by establishing design principles specifically from the perspective of VLA optimization. We identify a set of best practices based on information-theoretic insights, including maximized temporal token overlap, minimized vocabulary redundancy, enhanced multimodal mutual information, and token independence. Guided by these principles, we introduce \textbf{ActionCodec}, a high-performance action tokenizer that significantly enhances both training efficiency and VLA performance across diverse simulation and real-world benchmarks. Notably, on LIBERO, a SmolVLM2-2.2B fine-tuned with ActionCodec achieves a 95.5\% success rate without any robotics pre-training. With advanced architectural enhancements, this reaches 97.4\%, representing a new SOTA for VLA models without robotics pre-training. We believe our established design principles, alongside the released model, will provide a clear roadmap for the community to develop more effective action tokenizers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
