Tencent が Hy-Embodied-VLM-1.0 をリリース
Tencent から Hy-Embodied-VLM-1.0 がリリースされました。
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Hy-Embodied-VLM-1.0
Efficient Physical-World Agents
Tencent Robotics X × Hy Vision Team × Futian Laboratory
🔥 Updates
[2026-07-15] 🚀 We have released Hy-Embodied-VLM-1.0! An efficient Mixture-of-Experts vision–language foundation model for embodied agents in the physical world, activating only ~3B parameters per token (~30B total) for high inference efficiency. Weights are available on Hugging Face, together with inference code for both HuggingFace transformers and vLLM.
[2026-06-15] 🤖 We have released HY-VLA-0.5! The official code, UMI-trained weights and 2000+ hours of high-fidelity UMI data are now available.
[2026-04-09] 🚀 We have released HY-Embodied-0.5, featuring the open-sourced HY-Embodied-0.5 MoT-2B weights on Hugging Face along with the official inference code!
📖 Abstract
Building capable embodied agents requires not only multimodal perception and understanding, but also agentic capabilities for reasoning about actions, adapting to evolving situations, and interacting with the physical world. In this report, we introduce Hy-Embodied-VLM-1.0, an efficient and powerful embodied foundation model specifically designed for embodied agents operating in the physical world.
To cultivate such capabilities from the pre-training stage onward, we define an action-centric capability taxonomy comprising three progressive dimensions: Action-Relevant State Understanding, Action–Transition Reasoning, and Sequential and Adaptive Reasoning. Guided by this taxonomy, we develop a systematic data pipeline and curate data mixtures spanning both pre-training and post-training.
To deliver strong physical-world understanding and interaction capabilities while supporting latency-sensitive deployment, we build our model on the Hy3-A3B language backbone and the Hy-ViT2 vision encoder. Its efficient Mixture-of-Experts architecture combines strong model capacity with high inference efficiency. We evaluate Hy-Embodied-VLM-1.0 on a comprehensive suite of 38 benchmarks covering embodied perception, physical-world understanding, and embodied reasoning. The model achieves the best performance among similarly sized models on 19 of the 38 benchmarks and substantially outperforms strong competitors, including Qwen3.6-A3B and Cosmos 3. Compared with the previous-generation Hy-Embodied-0.5 MoT-2B, Hy-Embodied-VLM-1.0 improves average performance by 8.4%. Despite activating only 3B parameters, it achieves performance close to that of the previous-generation model with 32B activated parameters. Beyond static benchmark evaluation, Hy-Embodied-VLM-1.0 also demonstrates strong performance on embodied agentic tasks requiring multi-turn interaction and long-horizon reasoning.
⭐️ Key Features
🧠 Efficient MoE, ~3B activated — Combines the Hy3-A3B language backbone with the Hy-ViT2 vision encoder in a Mixture-of-Experts architecture. Only ~3B parameters are activated per token — approximately one-tenth of the activated parameters of the previous-generation A32B system, while achieving
SOURCE: Tencent (2026-07-14)
