Skip to content
Papers.

Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments research paper by Alibaba (Qwen), 2026

Alibaba (Qwen) · May 28, 2026 · Multimodal and robotics · 37 citations · 145 upvotes · unverified

Read on arXiv

What it shows

A unified vision-language-action model is presented that integrates diverse embodied decision-making tasks through a shared architecture and training approach, demonstrating strong performance across manipulation, navigation, and trajectory prediction with generalization across different robot platforms and environments.

UnverifiedHugging Face's summary; not yet checked by hand.

More from Alibaba (Qwen)

All 61
PaperCitations
HappyWorld-BenchEvaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, interaction, and modification.Agents and evaluation · Sep 2026 · Unverified0
One to More, More to One: Category-Aware Iterative Expert Training for Software Engineering AgentsRepository-level software engineering (SWE) comprises heterogeneous task categories, whose progress under pooled agentic reinforcement learning can be uneven: gains in some categories coincide with regressions in...Training and scaling · Sep 2026 · Unverified0
OmniVChat: Synthesizing, Benchmarking, and Training for Native Audio-Visual DialogueWe define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model.Multimodal and robotics · Sep 2026 · Unverified0
RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use AgentsComputer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line.Agents and evaluation · Sep 2026 · Unverified0
CORE: Improving Compositional Reasoning in MLLM Embedding via Reranker DistillationCORE distills compositional ranking judgments from a cross-attentive reranker into an embedding model via synthesized multi-level candidates and a Rank-KL objective, improving compositional retrieval without degrading standard performance.Reasoning · Sep 2026 · Unverified0
Terminal-Universe: Turning Agent Trajectories into Scalable Terminal EnvironmentsTerminal-Universe reconstructs executable workspaces from agent trajectories to synthesize diverse training tasks and improves post-training performance through supervised fine-tuning.Agents and evaluation · Sep 2026 · Unverified1
Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous DrivingQwen-Drive-1.0 is a vision-language foundation model for autonomous driving that unifies 3D perception, visual question answering, and motion planning via shared representations and staged training.Multimodal and robotics · Aug 2026 · Unverified3
On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training StabilityQwen3.8-Flash-Next: a 125B mixture-of-experts model with 6B active that nearly matches its 397B predecessor at 1/9 the training compute.Architectures · Aug 20266
Topic
PaperCitations
Segment AnythingA promptable model and a dataset of over a billion masks that cut out any object in any image.Meta · Apr 202315.7k
GPT-4o System CardGPT-4o is an omnimodal autoregressive model trained to handle text, audio, image, and video inputs, offering high-performance outputs across these modalities, with particular strengths in vision and audio.OpenAI · Oct 2024 · Unverified4,980
SAM 3: Segment Anything with ConceptsSegment Anything Model 3 achieves state-of-the-art performance in promptable concept segmentation and tracking by leveraging a unified model architecture with decoupled recognition and localization.Meta · Nov 2025 · Unverified999
DeepSeek-VL: Towards Real-World Vision-Language UnderstandingDeepSeek-VL is an open-source vision-language model that achieves state-of-the-art performance in real-world applications by combining a hybrid vision encoder with effective pretraining strategies to preserve language model capabilities.DeepSeek · Mar 2024 · Unverified889
Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and GenerationJanus, an autoregressive framework with separate visual encoding pathways within a unified transformer architecture, enhances performance in unified multimodal understanding and generation.DeepSeek · Oct 2024 · Unverified481
SAM 3D: 3Dfy Anything in ImagesSAM 3D is a generative model that reconstructs 3D objects from single images using a multi-stage training framework that includes synthetic pretraining and real-world alignment, achieving high performance in human preference tests.Meta · Nov 2025 · Unverified256
About this paper
Authors
Qiuyue Wang, Mingsheng Li, Jian Guan and 37 more
arXiv
2605.30280 · PDF
Venue
arXiv.org
Citations
37, 3 influential · Semantic Scholar
Upvotes
145 · Hugging Face
Lab
Alibaba (Qwen) · on Companies · on Quarterly

Changes

What changed
Influential citationsfirst count: 3Sep 25, 2026
Citationsfirst count: 37Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

New papers by email

Monday afternoons, only in weeks with new papers from the labs.

Double opt-in. Unsubscribe any time.