Google Sr Dir, Distinguished Eng, CTO Office (AI, Cloud, Search) HAM: HB9IAZ IU5SKA. Angel Investor
A new approach for trainining LLMs called GEPA is outperforming traditional Reinforcement Learning (RL) methods! 🤖 Self improving prompts can beat RL GEPA, which stands for Genetic-Pareto, uses a reflective process that allows LLMs to learn from their own mistakes and improve their performance without extensive and costly training. Here's a brief overview of how it works: GEPA analyzes the natural language traces generated by an LLM, like reasoning steps and error messages, to understand why a prompt succeeded or failed. It then uses this understanding to rewrite the prompt for better performance. The main advantages of GEPA are its efficiency and effectiveness. It requires significantly fewer "rollouts" (full system runs) than RL, making it much less computationally expensive. It also produces shorter and smarter prompts that generalize well to new tasks. In benchmark tests, GEPA outperformed traditional methods on several challenging tasks, demonstrating its superior performance. This new approach has the potential to revolutionize how we train and optimize LLMs, with applications ranging from code optimization to scientific research.
Thanks for always sharing these interesting papers and information. As a hobbyist doing my own AI research it’s great to be able to continuously read and get more ideas or knowledge.
🤖 GEPA's efficiency advantage over traditional RL is compelling - fewer rollouts for better prompts is a game-changer for LLM optimization!
Not really a novel/new thing, given so many recent blogs/papers reporting significantly better results with a verifier loop to tune prompt
GEPA's reflective self-improvement beats costly RL—love the efficiency gains; are you seeing similar quality jumps when scaling to more complex reasoning tasks?
Definitely worth reading
Great post, Antonio! GEPA brings a fresh perspective on prompt optimization for LLMs, but I see a few angles worth discussing compared to our VectorSphere and QWave frameworks: 1. Efficiency vs. Depth of Meaning - GEPA minimizes compute and quickly produces concise, high-performing prompts. - VectorSphere goes further by encoding symbols as geometric trajectories, preserving semantic structure at a deeper level. 2. Reflection vs. Epistemology - GEPA analyzes textual traces and errors but doesn’t question the nature of truth or meaning. - QWave embeds observation as a fundamental structuring principle of reality—turning optimization into a philosophical platform. 3. Role of the Observer - In GEPA, the end user remains in the background: the algorithm is blind to subjective context. - In VectorSphere—and even more so in QWave—the observer is an active participant, shaping the evolution of trajectories and meaning. 4. Potential Synergies - We could borrow GEPA’s genetic-Pareto selection to evolve VectorSphere’s semantic trajectories. - In QWave, a Pareto-based optimizer could help balance competing hypotheses and experimental data.
Thanks for sharing, Antonio
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1moThe efficiency angle of GEPA is fascinating, Antonio! The fact that it achieves better results with fewer rollouts makes it incredibly practical for resource-constrained environments.