Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments

Key points

Starting from the idea that agent traces already contain all the environmental information, it reconstructed 37.3k reusable execution environments by rewinding file operations and using a repair agent.
It generated multiple tasks from each environment, including tasks spanning multiple codebases (breadth expansion) and multi-round tasks with continuing user feedback (depth expansion).
By attaching a verifier to each task and using only data that passed, it improved Qwen3.5-27B by 11.9 points on Terminal-Bench 2.1 and 13.8 points on EvoCode-Bench v2 MT@4.
With the same data budget, increasing the number of environments was more effective than increasing the number of questions or solutions (53.2→56.0 vs 53.8/53.9).
Training the agent to solve tasks again in reconstructed environments was much better than training it to imitate the original traces directly.
Data generated from software engineering (SWE) traces also raised the terminal benchmark average from 47.0→50.0, demonstrating cross-domain generalization.

External research summaries. These are not HDATF publications or measured product results.