Qwen-VLA: Unified Vision-Language-Action Model Across Robot Tasks, Environments, and Embodiments
Alibaba's Qwen team presents Qwen-VLA, a unified embodied foundation model that extends the Qwen vision-language stack to continuous action and trajectory generation via a DiT-based action decoder. The model is jointly pretrained on diverse data spanning manipulation trajectories, egocentric demonstrations, synthetic simulation, and navigation data, with embodiment-aware prompt conditioning to support multiple robot platforms. A unified action-and-trajectory prediction framework covers manipulation, navigation, and trajectory prediction tasks. Benchmarks show strong results: 97.9% on LIBERO, 73.7% on Simpler-WidowX, 69.0% OSR on R2R navigation, and 76.9% average OOD success in real-world ALOHA experiments.
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Qwen2-VL: Alibaba Releases Latest Vision-Language Model with Extended Video Understanding
Alibaba's Qwen team has released Qwen2-VL, the latest iteration of their vision-language model series built on the Qwen2 foundation. The model claims state-of-the-art performance on visual understanding benchmarks including MathVista, DocVQA, RealWorldQA, and MTVQA. A notable capability is understanding videos exceeding 20 minutes in length for question answering, dialog, and content creation tasks.
Qwen VLo: Unified Multimodal Understanding and Generation Model
Alibaba's Qwen team has announced Qwen VLo, a new model that unifies multimodal understanding and image generation in a single architecture. Building on the Qwen2.5 VL lineage, the model is positioned to both comprehend and generate high-quality visual content. This represents a step toward unified perception-and-creation models, a direction several frontier labs are pursuing simultaneously.
Qwen2.5-VL-32B: Reinforcement-Learning-Optimized Vision-Language Model Released
Alibaba's Qwen team has released Qwen2.5-VL-32B-Instruct, a 32-billion-parameter vision-language model built on the Qwen2.5-VL series and further optimized with reinforcement learning. The model is open-sourced under the Apache 2.0 license and available on Hugging Face and ModelScope. It follows the January 2025 launch of the broader Qwen2.5-VL series, positioning the 32B scale as a balance between capability and deployment practicality.
LabVLA: Vision-Language-Action model and RoboGenesis data engine for scientific laboratory robotics
Researchers introduce LabVLA, a Vision-Language-Action model designed to bridge written scientific protocols and physical robot execution in laboratory settings. To address the data scarcity problem, they build RoboGenesis, a simulation-based data engine that composes lab workflows from atomic skills and generates structured demonstrations across robot embodiments. LabVLA uses a two-stage training recipe combining FAST action token pretraining on a Qwen3-VL-4B-Instruct backbone with flow matching posttraining via a DiT action expert. On the LabUtopia benchmark, LabVLA achieves the highest average success rate among evaluated baselines in both in-distribution and out-of-distribution settings.
Qwen2.5-VL: Alibaba's New Flagship Vision-Language Model Released in 3B/7B/72B Sizes
Alibaba's Qwen team has released Qwen2.5-VL, their new flagship vision-language model, representing a significant upgrade over Qwen2-VL. The release includes both base and instruct variants in three sizes (3B, 7B, 72B), all open-weighted and available on Hugging Face and ModelScope. The 72B instruct model is also accessible via Qwen Chat. Key capabilities highlighted include enhanced visual understanding, with the model positioned as a major step forward in multimodal performance.
QVQ-72B-Preview: Qwen Visual Reasoning Model Release
Alibaba's Qwen team has released QVQ-72B-Preview, a 72-billion parameter multimodal model designed to integrate visual understanding with advanced reasoning capabilities. The model is positioned as an extension of Qwen's language reasoning work into the visual domain. It is available on GitHub, Hugging Face, ModelScope, and Kaggle with a live demo.
Ω-QVLA: Training-Free W4A4 Quantization for Full Vision-Language-Action Models Including Diffusion Action Heads
Omega-QVLA is a post-training quantization framework that compresses both the LLM backbone and the diffusion-based action head of VLA models to uniform W4A4 precision without mixed-precision schemes or fine-tuning. It combines composite SVD-Hadamard rotation for weight energy equalization with per-step DiT activation scaling to handle dynamic-range drift across denoising steps. On the LIBERO benchmark, it achieves 98.0% and 87.8% task success on Pi 0.5 and GR00T N1.5 respectively—matching or exceeding FP16 baselines—while reducing static memory footprint by 71.3%. Real-world manipulation experiments confirm the approach generalizes beyond simulation.
TempoVLA: Speed-Controllable Vision-Language-Action Policy for Robot Manipulation
Researchers introduce TempoVLA, a Vision-Language-Action model that enables explicit speed control during robot manipulation by conditioning on a speed signal rather than inheriting a fixed speed from training data. The system pairs Variable-Speed Trajectory Augmentation (VSTA), which re-times demonstrations by merging or splitting actions, with a model-side conditioning mechanism. Experiments in simulation and real-world tasks show flexible bidirectional speed control, with dynamic adaptation—accelerating in low-risk transit phases and decelerating for high-risk contact stages—achieved by coupling with a large multimodal model.