LLM Agent Trajectory Synthesis

This field studies how to create useful sequences of agent actions, tool calls, observations, and decisions when real interaction data is scarce. Common approaches generate tasks and executable environments, collect teacher or simulated-agent rollouts, and verify them with state checks, constraints, or outcome rubrics; the resulting data is used for supervised fine-tuning or reinforcement learning. Researchers also reuse failed trajectories through relabeling, recovery examples, and skill extraction, especially for web, GUI, terminal, API, and long-horizon tasks. A central challenge is balancing scale and diversity with reliable, learner-relevant supervision that transfers to live or unseen environments rather than teaching brittle benchmark-specific behavior.