Large-scale agentic training and evaluation with large language models (LLMs) rely on isolated, stateful execution environments in which models inspect repositories, invoke tools, execute commands, and interact with task-specific services. These workloads create sandboxes in large bursts, span heterogeneous functionali...
Jia-Liang Huang, Hong-Xuan Tang, Jing-Chang Chen et al.· 3 citations
MiCRo is introduced, a two-stage framework that enhances personalized preference learning by leveraging large-scale binary preference datasets without requiring explicit fine-grained annotations and effectively captures diverse human preferences and significantly improves downstream personalization.
Jingyan Shen, Jiarui Yao, Rui Yang et al.· Conference on Empirical Meth...· 11 citations· ⚡3
A unified framework is introduced that distills intermediate representations that explicitly encode the physical modeling process and adopt a two-stage post-training strategy, where supervised fine-tuning establishes structured modeling, and reinforcement learning with rubric-based feedback improves the quality of the...
Ye Zhang, Xue-Hang Guo, Rui Pan et al.· 1 citation
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