As terminal-based code agents spread, an odd resource mismatch is widening: agent trajectories — full records of agent work sessions — are accumulating at scale, while the thing agent post-training actually needs, realistic executable environments, remains scarce. Terminal-Universe (arXiv:2609.04148, submitted Sep 3 by 14 authors from the Qwen team and partner institutions) takes the reverse route: environments do not have to be generated from scratch, because they are already hiding inside old trajectories.
The paper's premise is blunt. An environment can be re-queried into many verifiable tasks and provides execution feedback, whereas a trajectory is a single frozen demonstration — used once, then spent.
Trajectories Are Compressed Environments
The core observation: the tool-execution history recorded in a trajectory already exposes the structure and contents of the environment it ran in. So environments can be reconstructed backwards. Step one replays the file operations recorded in the trajectory, restoring each file to its pre-modification state and yielding a partial workspace. Step two dispatches a completion agent to supply the missing files and dependencies. On the recovered workspace, the framework both reconstructs the original intent task and synthesizes entirely new ones.
Two Scaling Axes: Breadth and Depth
Environments alone are not enough, so tasks are scaled along two axes. For breadth, the method mines directional dependency relations between related environments and synthesizes cross-workspace queries spanning multiple codebases — mirroring how real developers routinely touch several repos in one change. For depth, the initial single-turn query is extended into a multi-round session where a user agent simulates iterative feedback and requirement refinement.
The Numbers
Applied to public terminal agent trajectories, the framework produces 37.3k task-sufficient environments. Supervised fine-tuning of Qwen3.5-27B on this corpus improves single-round performance on Terminal-Bench 2.1 by 11.9 points and multi-round performance on EvoCode-Bench v2 MT@4 by 13.8 points. The day after release it ranked #1 on Hugging Face Daily Papers with 182 upvotes.
Three Takeaways
First, trajectories may be the most wasted asset of the agent era. Teams hoard them, burn them once in SFT, and move on; this work shows a trajectory is a compressed environment waiting to be unpacked into reusable, re-queryable workspaces. Second, authenticity: file structures come from environments that actually ran, not from model imagination — one less layer of hallucination risk compared to generating environments from scratch. Third, the interface matters: re-queryable tasks plus execution feedback are exactly the closed-loop ingredients for pushing post-training from SFT toward RL. The paper only runs SFT, but the foundation clearly points further.
For any team sitting on mountains of agent logs, the practical hint is simple: your trajectory store may already contain a batch of free environments — nobody has unpacked them yet.