3 citations · 12 across the 16 of their papers we have counts for
3 papers · 1 filter
MCP-Universe RL: A Framework for Training MCP Tool-Use Agents via Reinforcement Learning
Ziyang Luo, Yan Yang, Xiangru Jian +5
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.…
W&D:Scaling Parallel Tool Calling for Efficient Deep Research Agents
Xiaoqiang Lin, Jun Hao Liew, Silvio Savarese +1
Deep research agents have emerged as powerful tools for automating complex intellectual tasks through multi-step reasoning and web-based information seeking. While recent efforts h…
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars
Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai +5
Large language models (LLMs) have shown impressive capabilities in real-world applications. The capability of in-context learning (ICL) allows us to adapt an LLM to downstream task…