Fast-VLA: You Only Think Once for Robot Manipulation
We introduce Fast-VLA, which invokes a frozen 4B VLM once per episode to contextualize the instruction and initial views as fixed, multi-layer key-value task memory.
ProofPaper ↗
Key points
- Vision-language-action (VLA) policies commonly place a multi-billion-parameter model inside the feedback loop, repeatedly recomputing task context whenever they predict a new action chunk.
- Yet task understanding changes far more slowly than the robot state.
- During execution, a lightweight online actor fuses learned time- and arm-indexed queries with current visual and proprioceptive feedback to form action queries.
- These queries retrieve task context from the fixed memory and predict action chunks without re-running the foundation model.
Sources (1)
- [1]Fast-VLA: You Only Think Once for Robot ManipulationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 07:05 AM
We introduce Fast-VLA, which invokes a frozen 4B VLM once per episode to contextualize the instruction and initial views as fixed, multi-layer key-value task memory.
Vision-language-action (VLA) policies commonly place a multi-billion-parameter model inside the feedback loop, repeatedly recomputing task context whenever they predict a new action chunk.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 15, 2026VLA-$Δ$: Learning Physical State Transition for Vision-Language-Action Models
- Oct 15, 2026Skill-Decoupled On-Policy Expert Distillation for Unified Vision-Language Models
- Oct 15, 2026Can World and Action Modeling Benefit Each Other under Unified Causal Training?
- Oct 14, 2026ObjDream: Guiding Vision-Language-Action Models with Object-Level Understanding and Future State Prediction
- Oct 14, 2026mistralai/Timestral-209M
- Oct 14, 2026MorphAct: Robot Policies Morphing with the Worlds They Act In