[2604.09455] E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning

Computer Science > Artificial Intelligence arXiv:2604.09455 (cs) [Submitted on 10 Apr 2026] Title:E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning Authors:Weiyang Guo, Zesheng Shi, Liye Zhao, Jiayuan Ma, Zeen Zhu, Junxian He, Min Zhang, Jing Li View a PDF of the paper titled E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning, by Weiyang Guo and 7 other authors View PDF HTML (experimental) Abstract:While Large Language Models (LLMs) have demonstrated significant potential in Tool-Integrated Reasoning (TIR), existing training paradigms face significant limitations: Zero-RL suffers from inefficient exploration and mode degradation due to a lack of prior guidance, while SFT-then-RL is limited by high data costs and capability plateaus caused by low-entropy collapse. To address these challenges, we propose E3-TIR (Enhanced Experience Exploitation), a warm-up paradigm for the early stages of agent training. Specifically, we formulate training as the dynamic integration of three experience types: Expert Prefixes, Expert Guided, and Self-Exploration. By executing diverse branching exploration around expert "anchors" and employing a mix policy optimization mechanism, we effectively mitigate distribution shifts and resolve optimization conflicts arising from shared prefixes. Our method dynamically adapts the model's knowledge boundaries, effectively balancing exploration diversity with training this http URL results demonstrate that E3-TIR a...