Author

Xiaoqiang Lin

2 papers indexed here

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Preprint Aug 2026

MCP-Universe RL: A Framework for Training MCP Tool-Use Agents via Reinforcement Learning

Reinforcement learning (RL) has become an effective way to improve the tool-use ability of large language models (LLMs), but most existing RL frameworks stop at the policy update. For every new domain, the user is left with two hard systems problems: standing up an isolated environment for each of hundreds of concurren...

Ziyang Luo, Yan Yang, Xiang-Ru Jian et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Scaling Near-Optimal SFT-RL Annotation Budget Allocation from Small to Large LLMs

This work describes the near-optimal region, the set of allocations within a specified tolerance of peak performance, which is wide even for small tolerances, widens with model scale, and transfers reliably from small proxy models to large target models.

Jingtan Wang, A. Verma, Xiaoqiang Lin et al. · 0 citations