Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment

Agentic LLMs keep failing the same way because they lack specific, reusable capabilities.

Stanford's TRACE diagnoses those gaps from an agent's own trajectories, synthesizes one verifiable training environment per capability, trains a LoRA adapter for each, and routes tokens across experts—improving τ²-Bench by +15. 3 points and reaching 73. 2% Pass@1 on SWE-bench Verified.