🖥️ Executable training environments for coding agents are far scarcer than the trajectories we already have — this paper flips that imbalance around. Title: Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments URL: The framework reads the file-operation history embedded in existing agent trajectories, reconstructs executable, verifiable environments from them, and then synthesizes large batches of new training tasks on top. Key points 🔄 Reconstructing environments from trajectories It replays the read/write/edit operations in a trajectory to rebuild a partial workspace, then a completion agent fills in missing files and dependencies. A read-only verifier filters out workspaces that aren't actually task-sufficient. 🌐 Expanding tasks along width and depth Cross-WS synthesis links dependencies across multiple repos for "width," while Multi-Round dialogue adds user feedback for "depth." Multi-Round conversations average 4.51 rounds, with 69.6% following a fail-then-fix pattern. 📈 Double-digit performance gains Fine-tuning Qwen3.5-27B on this data lifts Terminal-Bench 2.1 by +11.9pt and EvoCode-Bench v2 by +13.8pt, beating every comparably-sized baseline it was tested against. It's a grounded way to scale agent training data by turning an already-abundant resource — leftover trajectories — into reusable, verifiable environments. #AgentTraining# #LLM#