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A popular way to optimize these systems is through reinforcement learning methods, such as Group Relative Policy Optimization (GRPO), a technique employed in popular reasoning models, including DeepSeek-R1. This method treats the system as a black box; it runs a task, gets a simple success metric (a “scalar reward,” like a score of 7/10), and uses this feedback to slowly nudge the model’s parameters in the right direction.

The major drawback of RL is its sample inefficiency. To learn effectively from these sparse numerical scores, RL methods often require tens of thousands, or even hundreds of thousands, of trial runs, known as “rollouts.” For any real-world enterprise application that involves expensive tool calls (e.g., API queries, code compilation) or uses powerful proprietary models, this process is prohibitively slow and costly.

As Lakshya A Agrawal, co-author of the paper and doctoral student at UC Berkeley, told VentureBeat, this complexity is a major barrier for many companies. “For many teams, RL is not practical due to its cost and complexity—and their go-to approach so far would often just be prompt engineering by hand,” Agrawal said. He noted that GEPA is designed for teams that need to optimize systems built on top-tier models that often can’t be fine-tuned, allowing them to improve performance without managing custom GPU clusters.

The researchers frame this challenge as follows: “How can we extract maximal learning signal from every expensive rollout to enable effective adaptation of complex, modular AI systems in low-data or budget-constrained settings?”

An optimizer that learns with language

GEPA (Genetic-Pareto) is a prompt optimizer that tackles this challenge by replacing sparse rewards with rich, natural language feedback. It leverages the fact that the entire execution of an AI system (including its reasoning steps, tool calls, and even error messages) can be serialized into text that an LLM can read and understand. GEPA’s methodology is built on three core pillars.

First is “genetic prompt evolution,” where GEPA treats a population of prompts like a gene pool. It iteratively “mutates” prompts to create new, potentially better versions. This mutation is an intelligent process driven by the second pillar: “reflection with natural language feedback.” After a few rollouts, GEPA provides an LLM with the full execution trace (what the system tried to do) and the outcome (what went right or wrong). The LLM then “reflects” on this feedback in natural language to diagnose the problem and write an improved, more detailed prompt. For instance, instead of just seeing a low score on a code generation task, it might analyze a compiler error and conclude the prompt needs to specify a particular library version.

The third pillar is “Pareto-based selection,” which ensures smart exploration. Instead of focusing only on the single best-performing prompt, which can lead to getting stuck in a suboptimal solution (a “local optimum”), GEPA maintains a diverse roster of “specialist” prompts. It tracks which prompts perform best on different individual examples, creating a list of top candidates. By sampling from this diverse set of winning strategies, GEPA ensures it explores more solutions and is more likely to discover a prompt that generalizes well across a wide range of inputs.

The effectiveness of this entire process hinges on what the researchers call “feedback engineering.” Agrawal explains that the key is to surface the rich, textual details that systems already produce but often discard. “Traditional pipelines often reduce this detail to a single numerical reward, obscuring why particular outcomes occur,” he said. “GEPA’s core guidance is to structure feedback that surfaces not only outcomes but also intermediate trajectories and errors in plain text—the same evidence a human would use to diagnose system behavior.”

For example, for a document retrieval system, this means listing which documents were retrieved correctly and which were missed, rather than just calculating a final score.

GEPA in action

The researchers evaluated GEPA across four diverse tasks, including multi-hop question answering (HotpotQA) and privacy-preserving queries (PUPA). They used both open-source (Qwen3 8B) and proprietary (GPT-4.1 mini) models, comparing GEPA against the RL-based GRPO and the state-of-the-art prompt optimizer MIPROv2.

Across all tasks, GEPA substantially outperformed GRPO, achieving up to a 19% higher score while using up to 35 times fewer rollouts. Agrawal provided a concrete example of this efficiency gain: “We used GEPA to optimize a QA system in ~3 hours versus GRPO’s 24 hours—an 8x reduction in development time, while also achieving 20% higher performance,” he explained. “RL-based optimization of the same scenario in our test cost about $300 in GPU time, while GEPA cost less than $20 for better results—15x savings in our experiments.”

Beyond raw performance, the researchers found that GEPA-optimized systems are more reliable when faced with new, unseen data. This is measured by the “generalization gap” (the difference between performance on training data and final test data). Agrawal hypothesizes that this is because GEPA learns from richer feedback. “GEPA’s smaller generalization gap may stem from its use of rich natural-language feedback on each outcome—what worked, what failed, and why—rather than relying solely on a single scalar reward,” he said. “This may encourage the system to develop instructions and strategies grounded in a broader understanding of success, instead of merely learning patterns specific to the training data.” For enterprises, this improved reliability means less brittle, more adaptable AI applications in customer-facing roles.

A major practical benefit is that GEPA’s instruction-based prompts are up to 9.2 times shorter than prompts produced by optimizers like MIPROv2, which include many few-shot examples. Shorter prompts decrease latency and reduce costs for API-based models. This makes the final application faster and cheaper to run in production.

The paper also presents promising results for utilizing GEPA as an “inference-time” search strategy, transforming the AI from a single-answer generator into an iterative problem solver. Agrawal described a scenario where GEPA could be integrated into a company’s CI/CD pipeline. When new code is committed, GEPA could automatically generate and refine multiple optimized versions, test them for performance, and open a pull request with the best-performing variant for engineers to review. “This turns optimization into a continuous, automated process—rapidly generating solutions that often match or surpass expert hand-tuning,” Agrawal noted. In their experiments on CUDA code generation, this approach boosted performance on 20% of tasks to an expert level, compared to 0% for a single-shot attempt from GPT-4o.

The paper’s authors believe GEPA is a foundational step toward a new paradigm of AI development. But beyond creating more human-like AI, its most immediate impact may be in who gets to build high-performing systems.

“We expect GEPA to enable a positive shift in AI system building—making the optimization of such systems approachable by end-users, who often have the domain expertise relevant to the task, but not necessarily the time and willingness to learn complex RL specifics,” Agrawal said. “It gives power directly to the stakeholders with the exact task-specific domain knowledge.”