arXiv AI July 29, 2026 By Minhyeok Lee, Chiyoung Kim, Chanhoe Gu, Seongrok Kim, Sanghyuk Roy Choi, Donghwan Hwang, Donghun Ryu, Seokhyun Kim

CoTinyVLA: Chain-of-Thought Distillation for a Sub-Billion-Parameter Vision-Language-Action Model

Read the original on arXiv AI →

arXiv:2607. 25487v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models translate natural-language commands into robot action sequences, but leading systems on the LIBERO-Plus robustness benchmark use three- to seven-billion-parameter backbones whose memory demands can exceed embedded robotic budgets.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models

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FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action Policies

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WatchAct: A Benchmark for Behavior-Grounded Robot Manipulation

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arXiv AI

Jun 9

CrossVLA: Cross-Paradigm Post-Training and Inference Optimization for Vision-Language-Action Models

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arXiv AI

Jun 30

RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis

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llms agents diffusion robotics multimodal benchmarks safety

Related stories

DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models

FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action Policies

WatchAct: A Benchmark for Behavior-Grounded Robot Manipulation

Restoring Linguistic Grounding in VLA Models via Train-Free Attention Recalibration

CrossVLA: Cross-Paradigm Post-Training and Inference Optimization for Vision-Language-Action Models

RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis