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Research questionHow can recorded terminal-agent trajectories be converted into executable, varied training environments?Terminal code-agent training needs realistic environments that agents can execute against and receive feedback from. Recorded trajectories are static demonstrations, so they do not directly provide reusable training settings.
AI Agents
Code Generation & Program Synthesis
LLM Pretraining & Post-training
Latest papersRecent research connected to this question, newest first.Terminal-Universe: Turning Agent Trajectories into Scalable Terminal EnvironmentsThis paper reconstructs partial workspaces by replaying recorded file operations, fills missing files and dependencies, and uses the recovered environments to create original and varied tasks. Evidence comes from post-training with public terminal-agent trajectories and evaluation on reported terminal and code-agent benchmarks.research paper · Sep 3, 2026
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How can terminal-agent training environments stay challenging as models improve without costly on-policy synthesis?How can LLM agents reuse execution traces without losing temporal and outcome-dependent behavior?How can coding and terminal agents be post-trained without distorting production-faithful token flows and control operations?How can we train and evaluate LLM agents for tool use across single- and multi-turn workflows with serial or parallel calls?
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