ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration (2026.acl-long)
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| Challenge: | Existing training frameworks for Large Language Models (LLMs) focus on answers’ accuracy, overlooking specific alignment for behavior patterns. |
| Approach: | They propose a training framework for calibrating agent’s tool-use behavior through two synergistic perspectives: self-evolving data flywheel and behavior calibration training. |
| Outcome: | The proposed framework improves the accuracy, efficiency, reasoning conciseness, and tool execution accuracy of large language models. |
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