We introduce OpenMobile-2, a near-frontier mobile agent with fully open training environments and recipes. We make three key advances: (1) Diverse environments with simulated commercial apps: We build MobileGym++, featuring 35 realistic, functionally rich commercial-style apps with cross-app workflows, while preserving...
Chen-Yang Yan, Ying-Ying Zhang, Kanzhi Cheng et al.· 0 citations
This work introduces TrajLong, a novel framework that compiles trajectories into long-context training tasks with dense supervision, targeting three representative atomic capabilities: evidence grounding, cross-evidence aggregation, and temporal state maintenance, and investigates the relationship between long-context...
Miao Peng, Qin-Tong Zhang, Nuo Chen et al.· 0 citations
Reinforcement learning (RL) has emerged as a key paradigm for improving the reasoning capabilities of large language models (LLMs). However, existing reward systems, such as rule-based and reward-model-based, often exhibit issues such as unstable optimization and reward hacking. In this work, we revisit the general rea...
He-Nan Sun, Ze-Hua Li, Hai-Tao Hu et al.· 0 citations
Experiments on five datasets spanning diverse physical environments show that PhyMo achieves state-of-the-art performance, compared to the strongest baseline on each dataset, demonstrating the superiority of PhyMo on multimodal representation learning in AI4Physics.
He-Nan Sun, Hai-Tao Hu, Jin Liu et al.· 0 citations
OSReward is introduced, a realistic, high-quality benchmark that evaluates VLM judges on CUA trajectories, and an open corpus of reasoning-annotated trajectory judgments for the CUA community, to close the gap in reliable CUA reward at scale.
Qiushi Sun, Kanzhi Cheng, Yian Wang et al.· arXiv.org· 1 citation· ⚡1
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