arXiv AI
LLaDA-UI: Bringing Block-wise Diffusion to Vision-Language GUI Agents
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arXiv AI
Jun 24
Accelerating Disaggregated RL for Visual Generative LLMs with Diffusion-Based Parallelism and Trainer-Assisted Generation
arXiv:2606. 24369v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a dominant post-training paradigm, driving the emergence of high-performance RL systems such as veRL for autoregressive large language models (LLMs).
Hugging Face Trending Papers
Jun 23
Accelerating Disaggregated RL for Visual Generative LLMs with Diffusion-Based Parallelism and Trainer-Assisted Generation
Reinforcement learning (RL) has become a dominant post-training paradigm, driving the emergence of high-performance RL systems such as veRL for autoregressive large language models (LLMs). In parallel, diffusion-oriented RL algorithms, e.
arXiv AI
Jul 3
MMBench-Live: A Continuously Evolving Benchmark for Multimodal Models
arXiv:2607. 01813v1 Announce Type: cross Abstract: Evaluation benchmarks are essential for assessing vision-language models (VLMs), but most multimodal benchmarks are static, making them vulnerable to temporal staleness, data contamination, and costly maintenance.
arXiv Machine Learning
Jul 7
XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control
arXiv:2607. 04171v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong multimodal understanding and spatial grounding, but their computational cost limits real-time robotic control.
arXiv Machine Learning
Jun 2
Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding in Vision-Language-Action Policies
arXiv:2508. 20072v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models adapt large vision-language backbones to map images and instructions into robot actions.
arXiv AI
5d ago
MobileWorldBench: Towards Semantic World Modeling For Mobile Agents
MobileWorldBench introduces a benchmark for evaluating vision‑language models as world models in mobile GUI agents, shifting from pixel‑based predictions to natural‑language state transitions. The authors release MobileWorld, a 1.4 million‑sample dataset that enhances VLM world‑modeling performance. They also present a framework that integrates these semantic world models into mobile agent planning, showing improved task success rates.