LLaDA-UI: Bringing Block-wise Diffusion to Vision-Language GUI Agents
LLaDA-UI, a 16.7B block-wise diffusion vision-language GUI agent, outperforms Qwen2.5-VL-7B and beats Qwen3-VL-8B on four of six GUI benchmarks.
LLaDA-UI is a 16.7B-parameter MoE-based, block-wise diffusion vision-language GUI agent built on the LLaDA2.0-mini-base diffusion language backbone with a native-resolution vision encoder. It uses a two-stage pipeline: general multimodal pre-training followed by GUI-agent supervised fine-tuning on mobile, desktop, web, and grounding data. It substantially outperforms Qwen2.5-VL-7B and surpasses Qwen3-VL-8B on four of six reported GUI benchmarks, establishing block-wise diffusion as a practical paradigm for latency-sensitive multimodal agents.
- 16.7B-parameter MoE diffusion GUI agent built on LLaDA2.0-mini-base backbone
- Two-stage training: multimodal pre-training, then GUI-agent supervised fine-tuning
- Outperforms Qwen2.5-VL-7B; beats Qwen3-VL-8B on four of six GUI benchmarks
- Preserves block-parallel decoding advantage for real-time screen-grounded action generation
From the original article
1 min read at huggingface.co
Diffusion large language models (dLLMs) achieve high decoding efficiency through block-parallel, arbitrary-order generation, making them attractive for latency-sensitive applications. GUI agents represent a natural testbed for this paradigm, as they must repeatedly perceive screen states and emit structured, spatially grounded actions in real time.
By Zhangxuan Gu, Haoxing Chen, Qi Qin · huggingface.co
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