TRACE: מערכת אימון אגנטים באמצעות סביבות סינתטיות

תקציר מקורי באנגליתAgentic LLMs often fail the same way, again and again. A Stanford research team traced this to missing, reusable capabilities. Their system, TRACE , diagnoses those gaps and trains for them directly. TRACE stands for T urning R ecurrent A gent failures into C apability-targeted training E nvironments. It was released open-source under an MIT license. What problem does TRACE solve? To understand the design, first consider why agents fail. They lack specific skills that tasks demand, like retrieving the right record or verifying a precondition. Two mainstream fixes spend compute poorly. Direct RL or SFT gives sparse rewards that never say which skill was missing. Broad synthetic data is untargeted, so budget flows to skills the model already has. However, TRACE observes that failures are not