Brilliant new paper from the Qwen team.
It provides insights into where agent training environments actually come from.
Terminal agent trajectories have accumulated at scale while realistic executable environments are scarce.
Environments are what post-training needs, since each one can be re-queried into many verifiable tasks and returns execution feedback, while a trajectory is a single frozen demonstration.
Terminal-Universe reconstructs the environment from the trajectory instead of generating one from scratch.
The tool-execution history in an existing trajectory already exposes the structure and contents of the environment it ran in. Replaying the recorded file operations restores each file to its pre-modification state, giving a partial workspace, and a completion agent then supplies the missing files and dependencies.
They scale the recovered workspaces two ways.
For breadth, mined dependency relations between related environments produce cross-workspace queries spanning multiple codebases. For depth, a single-turn query becomes a multi-round session where a user agent supplies iterative feedback and requirement refinement.
Applied to public terminal agent trajectories it yields 37.3k task-sufficient environments. Supervised fine-tuning of Qwen3.5-27B on that corpus improves Terminal-Bench 2.1 by 11.9 points and EvoCode-Bench v2 MT@4 by 13.8 points.