FORGE co-evolves prompts and training data for 16.52 points.

Automatic prompt optimization normally holds training data fixed, so repeated optimization only ever sees weaknesses present in those instances (arXiv 2609.15209). FORGE abstracts imperfect executions into reusable failure modes, synthesizes new training data through four mutation strategies, and feeds verified instances back into prompt search. Across eight benchmarks it improves the aggregate score 16.52 points over the unoptimized baseline, and the synthesized data transfer: all nine APO comparisons improve 2-9 points and all three GRPO comparisons 4-8 points under matched budgets.