Adaptive multimodal reasoning has emerged as a promising frontier in Vision-Language Models (VLMs), aiming to dynamically modulate between tool-augmented visual reasoning and text reasoning to enhance both effectiveness and efficiency. However, existing evaluations rely on static difficulty labels and simple metrics, overlooking the model-dependent nature of task difficulties: a task that is challenging for one model may be trivial for another. Consequently, they obscure the distinction between reasoning mode selection and overall task accuracy while neglecting fine-grained reasoning process analyses. In this paper, we propose AdaptMMBench, a comprehensive benchmark for adaptive multimodal reasoning across five domains: natural scene, OCR, GUI, knowledge, and math, encompassing both direct perception and complex reasoning tasks. AdaptMMBench utilizes a Matthews Correlation Coefficient (MCC) metric to evaluate the quality of reasoning mode selection, isolating this meta-cognition ability by dynamically identifying task difficulties based on models’ capability boundaries. Moreover, AdaptMMBench facilitates multi-dimensional reasoning process evaluation across key step coverage, tool effectiveness, and computational efficiency. Our evaluation shows that strong reasoning mode selection is helpful, but final adaptive gains and efficiency also depend on overall model capability. Process metrics further diagnose bottlenecks in reasoning coherence, tool use, and cost for improving adaptive performance and efficiency.

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).