GEPA (Genetic-Pareto) is a sample-efficient prompt optimization method for compound AI systems that works by reflectively evolving prompts using natural language feedback instead of traditional scalar rewards.

In each iteration, GEPA samples system rollouts (including reasoning traces, tool outputs, and any diagnostic text), reflects on them via an LLM to identify issues or propose improvements, and updates specific module prompts accordingly based on the feedback.

To ensure diversity and avoid local optima, GEPA maintains a pool of candidates and uses Pareto-based selection, which keeps all non-dominated strategies discovered so far and stochastically proposes new prompt variants, enabling robust generalization with far fewer rollouts than reinforcement

Automating Agentic Prompts: A new algorithm called GEPA, developed by researchers at UC Berkeley, Stanford, and other institutions, improves the performance of agentic systems by automatically refining their prompts.

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propose and test prompt updates, and combine complementary lessons from the Pareto frontier of its own attempts.

arxiv.org/abs/2507.19457

..GEPA and prompt optimization explained: https://arxiv.org/abs/2507.19457v1

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..make adapting large models more practical—especially when compute or data is limited. It’s like giving AI a way to learn from its own “thinking out loud,” turning natural language into a powerful tool for self-improvement.

Links:
Paper on arXiv: https://arxiv.org/abs/2507.19457 ..

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..code on the fly.

What’s cool here is the shift from treating AI tuning as a blind search for a higher score to a reflective process that leverages the AI’s native strength: language. By evolving prompts through thoughtful reflections, GEPA unlocks smarter, faster learning that could..

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..fewer attempts than traditional reinforcement learning methods. On several tough tasks like multi-step question answering and instruction following, GEPA consistently outperforms both standard reinforcement learning and previous prompt optimizers. It even shows promise for optimizing..

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..strategies by mixing and matching what works best.

GEPA treats AI prompt tuning like a conversation with itself, iterating through generations of prompts that learn from detailed feedback written in words, not just numbers. This lets it learn much more efficiently—up to 35 times..

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..what went wrong and how to fix it? That’s the idea behind a new approach called GEPA. Instead of relying solely on those sparse reward signals, GEPA has AI inspect its own attempts using natural language reflections. It diagnoses errors, proposes prompt fixes, and evolves smarter..

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