Trajectory2Gym：决策引导的工具使用合成训练工具使用智能体
Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents
Trajectory2Gym 通过构建具有组合覆盖和决策基础的交互来改进工具使用训练，而非收集成功轨迹。该系统在五个 Qwen3 模型规模上提升了 Tau1、Tau2 和 BFCL 指标，平均提升 16.9-29.8 百分点，尤其增强状态化多轮交互能力。
Abstract
Trajectory2Gym improves tool-use training by constructing interactions with compositional coverage and decision grounding rather than collecting successful trajectories.
Successful tool-use trajectories are not necessarily useful supervision. Even successful interactions may underrepresent multi-step dependencies or contain actions that are unjustified by the information visible to the agent at decision time. We identify two coupled requirements for effective trajectory supervision: compositional coverage, so that important observation-action dependencies are represented, and decision grounding, so that each supervised action is supported by the learner-visible interaction history. We introduce Trajectory2Gym, which constructs executable interactions around under-supported compositional structures and derives supervision only from grounded decisions in their realized executions. Structural-support selection allocates the resulting experience, while parent-normalized supervised fine-tuning preserves the semantic interaction as the unit of training weight. This reframes trajectory generation as the deliberate construction of learnable interaction experience, rather than the collection of successful trajectories. Across five Qwen3 scales, Trajectory2Gym improves Tau1, Tau2, and BFCL at every tested scale, increasing the equal-weight three-benchmark mean by 16.9-29.8 percentage points. The gains are strongest in stateful and multi-turn interaction, where later decisions depend on earlier observations and feedback. Our results suggest that scaling tool-use agents depends not only on collecting more successful trajectories, but on constructing interactions whose dependencies are represented and whose decisions are learnable from the agent's visible history.
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