Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text
Enabling Large Language Models (LLMs) to effectively utilize tools in multi-turn interactions is essential for building capable autonomous agents. However, acquiring diverse and realistic multi-turn tool-use data remains a significant challenge. In this work, we propose a novel text-based paradigm. We observe that textual corpora naturally contain rich, multi-step problem-solving experiences, which can serve as an untapped, scalable, and authentic data source for multi-turn tool-use tasks. Based on this insight, we introduce GEM, a data synthesis pipeline that enables the generation and extraction of multi-turn tool-use trajectories from text corpora through a four-stage process: relevance filtering, workflow & tool extraction, trajectory grounding, and complexity refinement. To reduce the computational cost, we further train a specialized Trajectory Synthesizer via supervised fine-tuning. This model distills the complex generation pipeline into an efficient, end-to-end trajectory generator. Experiments demonstrate that our GEM-32B achieve a 16.5% improvement on the BFCL V3 Multi-turn benchmark. Our models partially surpass the performance of models trained on τ - bench (Airline and Retail) in-domain data, highlighting the superior generalization capability derived from our text-based synthesis paradigm. Notably, our Trajectory Synthesizer matches the quality of the full pipeline while significantly reducing inference latency and costs.
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Zhihao Xu
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2026-01-16T10:30:47.984Z
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Xu Zhihao
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naiweizi
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Rumei Li
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Jiahuan Li
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Rongxiang Weng
admin_assigned
2026-01-19T14:38:27.977Z
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Rongxiang Weng
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wengrx
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Jingang Wang
admin_assigned
2026-01-19T14:38:22.544Z
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Jingang Wang
bitwjg
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Xunliang Cai
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Xiting Wang
admin_assigned
2026-01-19T14:38:07.595Z
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Wang
Xiting
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Xiting
Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text
Enabling Large Language Models (LLMs) to effectively utilize tools in multi-turn interactions is essential for building capable autonomous agents. However, acquiring diverse and realistic multi-turn tool-use data remains a significant challenge. In this work, we propose a novel text-based paradigm. We observe that textual corpora naturally contain rich, multi-step problem-solving experiences, which can serve as an untapped, scalable, and authentic data source for multi-turn tool-use tasks. Based on this insight, we introduce GEM, a data synthesis pipeline that enables the generation and extraction of multi-turn tool-use trajectories from text corpora through a four-stage process: relevance filtering, workflow & tool extraction, trajectory grounding, and complexity refinement. To reduce the computational cost, we further train a specialized Trajectory Synthesizer via supervised fine-tuning. This model distills the complex generation pipeline into an efficient, end-to-end trajectory generator. Experiments demonstrate that our GEM-32B achieve a 16.5% improvement on the BFCL V3 Multi-turn benchmark. Our models partially surpass the performance of models trained on τ - bench (Airline and Retail) in-domain data, highlighting the superior generalization capability derived from our text-based synthesis paradigm. Notably, our Trajectory Synthesizer matches the quality of the full pipeline while significantly reducing inference latency and costs.
A text-based data synthesis approach generates multi-turn tool-use trajectories for large language models, achieving improved performance and reduced computational costs through a specialized trajectory synthesizer.
large language models multi-turn interactions tool-use data text corpora data synthesis pipeline relevance filtering workflow extraction trajectory grounding complexity refinement supervised fine-tuning trajectory synthesizer BFCL V3 Multi-turn benchmark τ-bench inference latency