Tsinghua and Qwen team's Terminal-Universe turns agent trajectories into scalable terminal environments
rohanpaul_ai · x · 2026-09-10
A new Tsinghua + Qwen team paper flips the usual agent-data recipe: instead of imitating original runs, reconstruct the actual code workspace from a trajectory's tool-execution history and re-solve it.
- A trajectory is a single frozen demo; environments can be re-queried into many verifiable tasks with execution feedback.
- Terminal-Universe replays recorded file operations to restore pre-modification files, then a completion agent fills in missing files and dependencies.
- On the recovered workspace it both reconstructs the original task and synthesizes new ones, scaling along breadth (dependency mining) and depth.
Result: training on re-solved reconstructed workspaces yields better coding agents than imitating the original trajectories, easing the scarcity of executable environments for agent post-training.
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