基于容器化环境的大规模终端智能体轨迹生成

摘要

针对终端任务训练智能体模型，关键在于获取能够捕捉跨领域长程交互的高质量终端轨迹数据。然而大规模构建此类数据面临两大挑战：其一是可执行性要求，每个实例都需要配置适宜且往往各异的Docker环境；其二是可验证性难题，异构任务输出难以实现统一标准化验证。为此我们提出TerminalTraj可扩展流水线，通过三重机制突破瓶颈：（一）筛选高质量代码库构建Docker化执行环境；（二）生成与Docker环境对齐的任务实例；（三）合成带有可执行验证代码的智能体轨迹。基于该方案，我们成功构建32,000个Docker镜像并在八大领域生成50,733条已验证终端轨迹。采用Qwen2.5-Coder架构的模型在此数据上训练后，在TerminalBench评估中实现持续性能提升：TB~1.0版本最高提升20%，TB~2.0版本提升10%。特别值得注意的是，TerminalTraj-32B模型在百亿参数以下模型中表现突出，TB~1.0得分达35.30%，TB~2.0得分22.00%，并展现出优化的测试时扩展特性。所有代码与数据已开源：https://github.com/Wusiwei0410/TerminalTraj。

English

Training agentic models for terminal-based tasks critically depends on high-quality terminal trajectories that capture realistic long-horizon interactions across diverse domains. However, constructing such data at scale remains challenging due to two key requirements: \emph{Executability}, since each instance requires a suitable and often distinct Docker environment; and \emph{Verifiability}, because heterogeneous task outputs preclude unified, standardized verification. To address these challenges, we propose TerminalTraj, a scalable pipeline that (i) filters high-quality repositories to construct Dockerized execution environments, (ii) generates Docker-aligned task instances, and (iii) synthesizes agent trajectories with executable validation code. Using TerminalTraj, we curate 32K Docker images and generate 50,733 verified terminal trajectories across eight domains. Models trained on this data with the Qwen2.5-Coder backbone achieve consistent performance improvements on TerminalBench (TB), with gains of up to 20\% on TB~1.0 and 10\% on TB~2.0 over their respective backbones. Notably, TerminalTraj-32B achieves strong performance among models with fewer than 100B parameters, reaching 35.30\% on TB~1.0 and 22.00\% on TB~2.0, and demonstrates improved test-time scaling behavior. All code and data are available at https://github.com/Wusiwei0410/TerminalTraj.

基于容器化环境的大规模终端智能体轨迹生成

Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments

摘要

Support