When optimizing agents through reflection, extracting causal root causes from execution traces—rather than using raw or naively truncated traces—significantly improves learning efficiency and prevents overfitting to low-value failures.
This paper presents STRACE, a framework that helps LLM-based agents learn from their failures more effectively. Instead of using raw execution traces directly, STRACE filters out redundant failures at the batch level and identifies causally important steps within each trace, creating cleaner optimization signals for agent improvement.