GEPA reframes AI optimization: turning rich traces into evolved prompts via modular, Pareto-driven search.
GEPA achieves 35x fewer rollouts than GRPO yet matches its best validation scores on diverse tasks.
Pareto frontier tracking preserves diverse strategies that solve only-specific-instance issues, defying single-mmetric optimization norms.
Serializing traces with natural language reflection yields richer diagnostic signals than traditional scalar rewards in complex AI pipelines.
System-aware crossover blends modules from distinct lineages, enabling supercharged retrievers and generators to co-emerge in a single generation.
GEPA achieves 35x fewer rollouts than GRPO yet matches its best validation scores on diverse tasks.
Pareto frontier tracking preserves diverse strategies that solve only-specific-instance issues, defying single-mmetric optimization norms.
Serializing traces with natural language reflection yields richer diagnostic signals than traditional scalar rewards in complex AI pipelines.
System-aware crossover blends modules from distinct lineages, enabling supercharged retrievers and generators to co-emerge in a single generation.
