智能体老犯同样错误？斯坦福的TRACE能自动找出短板，给每个能力单独训练，直接提升15个点。
斯坦福研究者提出TRACE系统，从智能体轨迹中诊断能力缺口，为每个能力合成一个可验证的训练环境。系统为每个能力训练一个LoRA适配器，并在专家间路由token。在τ²-Bench上性能提升+15.3点，在SWE-bench Verified上达到73.2% Pass@1。
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. The post Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment appeared first on MarkTechPost .