ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration
Large Language Models (LLMs) can extend their parameter knowledge limits by adopting the Tool-Integrated Reasoning (TIR) paradigm. However, existing LLM-based agent training framework often focuses on answers' accuracy, overlooking specific alignment for behavior patterns. Consequently, agent often exhibits ineffective actions during TIR tasks, such as redundant and insufficient tool calls. How to calibrate erroneous behavioral patterns when executing TIR tasks, thereby exploring effective trajectories, remains an open-ended problem. In this paper, we propose ET-Agent, a training framework for calibrating agent's tool-use behavior through two synergistic perspectives: Self-evolving Data Flywheel and Behavior Calibration Training. Specifically, we introduce a self-evolutionary data flywheel to generate enhanced data, used to fine-tune LLM to improve its exploration ability. Based on this, we implement an two-phases behavior-calibration training framework. It is designed to progressively calibrate erroneous behavioral patterns to optimal behaviors. Further in-depth experiments confirm the superiority of  across multiple dimensions, including correctness, efficiency, reasoning conciseness, and tool execution accuracy. Our ET-Agent framework provides practical insights for research in the TIR field. Codes can be found in https://github.com/asilverlight/ET-Agent
6965b7a8fc8c4ecc02c7f8b1
Yifei Chen
admin_assigned
2026-01-16T15:17:13.536Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6621ec2524eb2673fe0790fc/cooTXi12eRWFiSSIj_nA-.jpeg
Ania Forge
zhangboguodong
user
zhangboguodong
6965b7a8fc8c4ecc02c7f8b2
Guanting Dong
claimed_verified
2026-01-13T08:24:17.728Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/61cd4b833dd34ba1985e0753/BfHfrwotoMESpXZOHiIe4.png
KABI
dongguanting
user
dongguanting
6965b7a8fc8c4ecc02c7f8b3
Zhicheng Dou
claimed_verified
2026-02-10T09:08:26.812Z
66f0bf59e9d50ec57febf751
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Zhicheng Dou
douzc
user
douzc
ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration
Large Language Models (LLMs) can extend their parameter knowledge limits by adopting the Tool-Integrated Reasoning (TIR) paradigm. However, existing LLM-based agent training framework often focuses on answers' accuracy, overlooking specific alignment for behavior patterns. Consequently, agent often exhibits ineffective actions during TIR tasks, such as redundant and insufficient tool calls. How to calibrate erroneous behavioral patterns when executing TIR tasks, thereby exploring effective trajectories, remains an open-ended problem. In this paper, we propose ET-Agent, a training framework for calibrating agent's tool-use behavior through two synergistic perspectives: Self-evolving Data Flywheel and Behavior Calibration Training. Specifically, we introduce a self-evolutionary data flywheel to generate enhanced data, used to fine-tune LLM to improve its exploration ability. Based on this, we implement an two-phases behavior-calibration training framework. It is designed to progressively calibrate erroneous behavioral patterns to optimal behaviors. Further in-depth experiments confirm the superiority of  across multiple dimensions, including correctness, efficiency, reasoning conciseness, and tool execution accuracy. Our ET-Agent framework provides practical insights for research in the TIR field. Codes can be found in https://github.com/asilverlight/ET-Agent
ET-Agent is a training framework that calibrates tool-use behavior in large language models through self-evolving data flywheels and behavior calibration training to improve task execution effectiveness.
Tool-Integrated Reasoning large language models agent training framework behavior calibration training self-evolving data flywheel tool-use behavior exploration ability two-phases training