arXiv Computation and Language
Sep 16

Spurious Tool Use: When RL Agents Learn the Wrong Reason to Act

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 AI
Oct 2

Reducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning

The paper introduces MSARL, a Multi‑Small‑Agent Reinforcement Learning framework that separates reasoning from tool use. A single Reasoning Agent plans and decomposes tasks, while multiple specialized Tool Agents handle specific external tools, each trained with imitation learning and reinforcement learning using role‑specific rewards. Experiments on mathematical problem solving with code execution show that MSARL improves reasoning stability and final‑answer accuracy compared to single‑agent baselines, and the architecture generalizes to other tool‑use tasks.

By Dayu Wang, Yutong Liu, Jiaye Yang, Weikang Li, Jiahui Liang, Yang Li
arXiv AI
Oct 12

Rollout-Time Intervention: Unlocking Agentic Reinforcement Learning Beyond Policy Limits

The paper introduces Rollout-Time Intervention (RTI), a method that uses reward-side evidence during rollouts to transform unsuccessful trajectories into successful ones, thereby providing learning signals for tasks beyond a policy’s intrinsic exploration limits. RTI filters out trajectories with excessive distributional shifts, allowing these off-policy interventions to guide policy optimization effectively. Experiments on five benchmarks show that RTI consistently outperforms existing RL baselines, enabling post-trained policies to approach the performance of stronger large language models and surpass their original capability boundaries.

By Chen Xi, Ding Ruixue, Wang Zhigaoyuan, Zhang Fanrui, Zhang Qiang, Chen Boli, Wang Shihang, Wang Tianfu, Ren Ailin, Wang Pengyong, Xie Pengjun, Xiong Hui