Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments
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.
698c7937eb12ea7453916853
Siwei Wu
claimed_verified
2026-02-24T09:51:55.691Z
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SiweiWu
SiweiWu
user
SiweiWu
698c7937eb12ea7453916854
Yizhi Li
claimed_verified
2026-04-07T08:48:59.753Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6382252f54421460665ec501/gW9fev3T5QPcNq4f9hqB1.jpeg
Yizhi Li
yizhilll
user
yizhilll
698c7937eb12ea7453916855
Yuyang Song
698c7937eb12ea7453916856
Wei Zhang
698c7937eb12ea7453916857
Yang Wang
698c7937eb12ea7453916858
Riza Batista-Navarro
698c7937eb12ea7453916859
Xian Yang
698c7937eb12ea745391685a
Mingjie Tang
698c7937eb12ea745391685b
Bryan Dai
698c7937eb12ea745391685c
Jian Yang
698c7937eb12ea745391685d
Chenghua Lin
Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments
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.
A scalable pipeline called TerminalTraj addresses challenges in creating high-quality terminal trajectories for training agentic models by filtering repositories, generating Docker-aligned task instances, and synthesizing executable agent trajectories across multiple domains.
terminal trajectories Docker environments executability verifiability TerminalBench Qwen2.5-Coder terminal-based tasks agent trajectories Docker-aligned task instances