Paper Proposes Joint Optimization of Prompts and Training Data via Failure Signals

A new arXiv paper addresses automatic prompt optimization, a technique that normally revises prompts using task feedback while leaving the training set unchanged. The authors argue that repeatedly tuning against the same examples limits feedback to already-known weaknesses, and they propose a failure-guided approach in which prompts and training data are improved together. This co-evolution aims to surface new shortcomings rather than only correcting previously identified ones.