TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation
On the map: Vision-language-action models → Generalist VLA models and benchmarks
Abstract (arXiv)
Vision-Language-Action (VLA) models have shown remarkable potential in visuomotor control and instruction comprehension through end-to-end learning processes. However, current VLA models face significant challenges: they are slow during inference and require extensive pre-training on large amounts of robotic data, making real-world deployment difficult. In this paper, we introduce a new family of compact vision-language-action models, called TinyVLA, which offers two key advantages over existing VLA models: (1) faster inference speeds, and (2) improved data efficiency, eliminating the need for pre-training stage. Our framework incorporates two essential components to build TinyVLA: (1) initializing the policy backbone with robust, high-speed multimodal models, and (2) integrating a diffusion policy decoder during fine-tuning to enable precise robot actions. We conducted extensive evaluations of TinyVLA in both simulation and on real robots, demonstrating that our approach significantly outperforms the state-of-the-art VLA model, OpenVLA, in terms of speed and data efficiency, while delivering comparable or superior performance. Additionally, TinyVLA exhibits strong generalization capabilities across various dimensions, including language instructions, novel objects, unseen positions, changes in object appearance, background variations, and environmental shifts, often matching or exceeding the performance of OpenVLA. We believe that methodname offers an interesting perspective on utilizing pre-trained multimodal models for policy learning. Our project is at https://tiny-vla.github.io.
Key facts. Cited by 331 papers in the map (data through 7 Oct 2026). Placed in Generalist VLA models and benchmarks, one of 13 directions in Vision-language-action models. Builds on 72 of its 88 references that are in the map. First posted on arXiv 19 Sep 2024.
Builds on
72 of 88 references.
Closest ancestors · 3
Same direction, newest first
- 2024LLaRA: Supercharging Robot Learning Data for Vision-Language Policy28 Jun
- 2024OpenVLA: An Open-Source Vision-Language-Action Model13 Jun
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control28 Jul
Other references · 69
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- 2023Open X-Embodiment: Robotic Learning Datasets and RT-X Modelscited by 849
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- 2023MiniGPT-v2: large language model as a unified interface for vision-language multi-task learningcited by 617
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- 2022BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learningcited by 517
- 2022Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Taskscited by 420
- 2024Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperationcited by 377
- 2021Pix2seq: A Language Modeling Framework for Object Detectioncited by 345
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- 2023SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planningcited by 289
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- 2023MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Modelscited by 255
- 2021Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasetscited by 248
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- 2024MobileVLM V2: Faster and Stronger Baseline for Vision Language Modelcited by 172
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- 2022A Unified Sequence Interface for Vision Taskscited by 152
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- 2022A Generalist Framework for Panoptic Segmentation of Images and Videoscited by 99
- 2021Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Interventioncited by 90
- 2023GOAT: GO to Any Thingcited by 89
- 2024LLARVA: Vision-Action Instruction Tuning Enhances Robot Learningcited by 87
50 most cited of 69 All 69 (JSON)
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Citing papers per year: 2024: 13; 2025: 152; 2026: 166.
331 papers.
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- 2025GR00T N1: An Open Foundation Model for Generalist Humanoid Robotscited by 1,167
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- 2025VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizerscited by 67
- 2025Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Surveycited by 66
- 2025WorldEval: World Model as Real-World Robot Policies Evaluatorcited by 64
- 2025RoboArena: Distributed Real-World Evaluation of Generalist Robot Policiescited by 63
- 2025Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videoscited by 63
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50 most cited of 331 All 331 (JSON)
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- 2025Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Modelcited by 125
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- 2026Discrete Forcing: Infusing Discrete Guidance into Continuous Denoising for Few-Step Action Experts30 Sep 2026
- 2026Spike-driven Vision-Language-Action Model30 Sep 2026
- 2026DSDyn-VLA: A Dual-Stream Dynamic Manipulation Framework with Motion Perception, Future Awareness, and Realtime Correction30 Sep 2026
- 2026Taming VLAs under Robot Execution Errors: Self-Compensation and Stress Testing29 Sep 2026
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- 2026Alignment-Guided Flow Transformer for Efficient Vision-Language-Action Policy Learning28 Sep 2026
- 2026ActionGround: Training-Free Runtime Refinement of Frozen VLA Policies27 Sep 2026
- 2026Self-Adaptive VLA for Robust Robot Deployment24 Sep 2026
- 2026VLAQuantBench: Closed-Loop Evaluation of Post-Training Quantization for Vision-Language-Action Models21 Sep 2026
- 2026StateMem: Single-State Residual Memory with Adaptive Inference for Vision-Language-Action Policies19 Sep 2026
- 2026SkipVLA: Skipping VLA Steps with Classical Planning for Fast Robot Manipulation17 Sep 2026
12 newest of 191 All 191 (JSON)
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