Self-reflection has become a key mechanism for improving reasoning in Vision-Language Models (VLMs), yet this corrective mechanism often fails when resolving complex fine-grained visual ambiguities. This performance degradation stems from the issue of modality disconnect in self-reflection: most existing models execute self-reflection either within textual or latent space, lacking an explicit mechanism to align textual reasoning with visual evidence. In this paper, we propose MIRROR, a closed-loop visual reflection framework comprising four steps: response generation, error identification, region-based visual verification, and revision. In this cycle, the model first generates an initial response, identifies uncertain logical statements in the response that require visual verification, then grounds them in relevant image regions, and finally revises based on the visual evidence. We construct a multi-turn visual reflection dataset ReflectV, which empowers the model with such a reflective capability. Extensive experiments across diverse multimodal benchmarks show that MIRROR improves performance and reduces visual hallucinations by an average of about 7.2%, demonstrating the advantage of transforming self-reflection from open-loop textual revision into closed-loop, visually grounded verification.

Vision-Language Models (VLMs) have catalyzed the development of Graphical User Interface (GUI) agents for desktop automation. However, reinforcement learning (RL) for GUI agents remains bottlenecked by two challenges: existing RL frameworks still leave substantial idle time inside the rollout stage, and the learning signal from long-horizon, sparse-reward interactions is highly imbalanced. In this paper, we introduce DART, a fully Decoupled Agentic RL Training framework for GUI agents. Whereas prior RL designs only separate the trainer from the rollout, DART splits the pipeline into four independent modules: environment cluster, rollout service, data manager, and trainer, which brings two extra efficiency benefits: rollout-wise trajectory scheduling and per-worker model synchronization. This yields a 1.6× increase in GPU utilization, a 1.9× boost in training throughput, and a 5.5× increase in environment utilization over a non-decoupled baseline. We further propose a multi-level adaptive data curation scheme operating at the task, trajectory, step, and token levels: it reallocates rollout budget by task difficulty, bootstraps hard tasks with a pre-collected experience pool, restricts policy updates to high-entropy critical steps, and applies truncated importance sampling to absorb the distribution gap between th rollout and training engines. On the OSWorld benchmark, our DART-GUI-7B achieves a 42.13 task success rate, a 14.61 absolute improvement over its base model and a new state of the art among open-source 7B-scale agents. Code, data, and model checkpoints are open-sourced at https://dart-gui-anonymous.github.io (anonymous).

Graphical User Interface (GUI) Agents, powered by multimodal large language models (MLLMs), have shown great potential for task automation on computing devices such as computers and mobile phones. However, existing agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effectiveness. We introduce InfiGUIAgent, an MLLM-based GUI Agent trained with a two-stage supervised fine-tuning pipeline. Stage 1 enhances fundamental skills such as GUI understanding and grounding, while Stage 2 integrates hierarchical reasoning and expectation-reflection reasoning skills using synthesized data to enable native reasoning abilities of the agents. InfiGUIAgent achieves competitive performance on several GUI benchmarks, highlighting the impact of native reasoning skills in enhancing GUI interaction for automation tasks.

The rapid advancements in Large Language Models (LLMs) have revolutionized various natural language processing tasks. However, the substantial size of LLMs presents significant challenges in training or fine-tuning. While parameter-efficient approaches such as low-rank adaptation (LoRA) have gained popularity, they often compromise performance compared to full-rank fine-tuning. In this paper, we propose Outlier-weighed Layerwise Sampling (OWS), a new memory-efficient fine-tuning approach, inspired by the layerwise outlier distribution of LLMs. Unlike LoRA, which adds extra adapters to all layers, OWS strategically assigns higher sampling probabilities to layers with more outliers, selectively sampling only a few layers and fine-tuning their pre-trained weights. To further increase the number of fine-tuned layers without a proportional rise in memory costs, we incorporate gradient low-rank projection, further boosting the approach’s performance. Our extensive experiments across various architectures, including LLaMa2 and Mistral, demonstrate that OWS consistently outperforms baseline approaches, including full fine-tuning. Specifically, it achieves up to a 1.1% average accuracy gain on the Commonsense Reasoning benchmark, a 3.0% improvement on MMLU, and a notable 10% boost on MT-Bench, while being more memory efficient. OWS allows us to fine-tune 7B LLMs with only 21GB of memory. Our code is available at https://github.com/pixeli99/OWS.