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

1Key Takeaways

• 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.