ToolSearcher is a reinforcement learning framework designed to improve large‑scale tool selection for large language models. It introduces category‑constrained discrimination, event‑level search modeling, and trajectory‑aligned credit allocation to better distinguish similar tools, optimize multi‑turn search, and provide fine‑grained rewards. Experiments on large‑scale benchmarks show that ToolSearcher outperforms strong baselines in iterative search and complex tool composition scenarios.
By Zhenlong Dai, Xujie Song, Zitong Wang, Tong Niu, Jian liu, Weiqiang Wang, Xiu Tang, Sai Wu, Chang Yao, Jingyuan Chen
The paper investigates how reinforcement learning can cause large language model agents to adopt shortcut policies for tool use, relying on superficial prompt cues rather than actual task needs. By creating synthetic environments that mix factual QA and math reasoning, the authors show that agents often invoke tools when cues are present, even when those tools are unnecessary, with spurious invocation rates rising up to 39%. They find that shortcut learning occurs mainly when agents have already mastered the target tool and that semantic alignment between cues and tools amplifies the effect. To counter this, they propose a dense, decision-level reward where an LLM judge assesses tool necessity, which reduces cue-driven tool use while maintaining performance.
By Yiwei Yang, Haoxiang Zhang, Bingbing Wen, Yao Lu, Yuchen Wu, Lei Zhang, Julian McAuley, Pan Lu, Bill Howe
arXiv:2607. 14145v1 Announce Type: new Abstract: Tool-augmented large language model agents excel at long-horizon tasks, yet they are typically post-trained on fixed toolsets.
By Weiting Liu, Jieyi Bi, Wanqi Zhou, Jianfeng Feng, Yining Ma, Ai Han, Wenlian Lu
arXiv:2608.22479v1 Announce Type: new
Abstract: Retrieval-augmented generation (RAG) enables LLMs to access external knowledge for answering knowledge-intensive questions. For complex multi-hop quest...
By Jun Chen, Yongchao Liu, Pengyu Qiu, Jiajun Zheng, Juelu Zhang, Yujie Zeng, Qin Zhang, Ziyue Qiao, Xiao Luo
arXiv:2604. 27660v3 Announce Type: replace Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge.
By Shuzheng Si, Haozhe Zhao, Yu Lei, Qingyi Wang, Dingwei Chen, Zhitong Wang, Zhenhailong Wang, Kangyang Luo, Zheng Wang, Gang Chen, Fanchao Qi, Minjia Zhang, Maosong Sun
MATCH is a closed‑loop framework for model‑aware tool learning that combines curriculum scheduling with hierarchically gated rewards. It introduces Model‑Aware Curriculum Learning (MACL), which dynamically adjusts sample difficulty based on reward signals, and Hierarchical Tool‑call Gated Reward (HTGR), which allocates credit at the tool name, argument key, and argument value levels only when prerequisites are met. Experiments on API‑Bank and BFCL V3 show MATCH achieving 72.19% and 62.87% overall accuracy, outperforming both supervised and RL‑based baselines across multiple backbone models.
By Shihao Liu, Hao Yin, Lijun Liu, Zhengzong Chen, Yuanyuan Zhao, Fei Huang
arXiv:2607. 19345v1 Announce Type: cross Abstract: Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to long-context settings has emerged as an important frontier.
By Lizhe Fang, Weizhou Shen, Tianyi Tang, Yisen Wang
arXiv:2606. 06284v1 Announce Type: new Abstract: Large language model agents increasingly rely on external tools, but larger tool menus can reduce reliability and efficiency by increasing wrong-tool calls, premature actions, and token cost.
By Rahul Suresh Babu, Laxmipriya Ganesh Iyer
The paper introduces SMITH, a reinforcement learning framework that jointly trains large language models to create and use tools within a single policy. By alternating between build and use tasks and employing separate reward signals for schema, code, and outcome failures, SMITH enables a 4B Qwen3 model to achieve state‑of‑the‑art accuracy on procedural reasoning benchmarks, outperforming larger untrained models and improving performance on downstream tasks when its tools are applied.
By Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen, Shao-Hua Sun, Hung-yi Lee
arXiv:2509. 23071v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) agent development is hindered by the lack of executable ground-truth agent-environment interaction trajectories.
By Muzhi Li, Jinhu Qi, Yihong Wu, Minghao Zhao, Liheng Ma, Yifan Li, Xinyu Wang, Zhenghan Tai, Zixing Song, Yingxue Zhang, Ho-fung Leung, Irwin King
arXiv:2609.24290v1 Announce Type: new
Abstract: Instruction-tuned LLMs faced with underspecified queries often commit to a single interpretation rather than ask for clarification, producing confident...
By Yunxiang Li, Xixin Wu, Helen Meng
arXiv:2606. 03762v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) equips large language models (LLMs) with tool-use capabilities that substantially improve reasoning on complex tasks.
By Hongye Cao, Nuo Yan, Haoyuan Deng, Ziwei Wang, Tianpei Yang, Jing Huo, Yuyao Zhang, Yang Gao