GEPA: Reflective prompt evolution can outperform reinforcement learning

It has been commonly observed that the current crop of LLMs can be too agreeable/sycophantic (or on some topics, too disagreeable) due to the commonly chosen RLHF priorities.

Simply asking the LLM in two separate contexts the same question but from opposing perspectives, then in a third context asking it to analyze both responses and choose the most neutral and objective take, you wipe out any "(dis)agreeableness" bias and dig closer to a deeper, more nuanced synthesis of a given topic. This paper is just taking this idea to the next level.

This isn't really possible with RLHF alone unless you train the LLM to often give two opposing perspectives, which would get tiring.

I guess Turing couldn’t see the trillions of base pairs of DNA, complex methylation states, dendritic spines of the neurons, etc., just for starters.

Is the name meant to be a jab at you know who or am I reading too much into it?

To me that's a complete circle back to lessons learned from neural networks - I long suspected we'd be heading that way again with transformers being a computational step rather than the whole thing.

This is my research area. I just finished reviewing six NeurIPS papers (myself, no LLM involved) on LLM Agents for discovery and generation and I'm finding that evaluating LLM agents on raw performance for a task isn't as insightful anymore -- every paper is claiming state of the art 10x performance boost by {insert random acronym that devolves into combinatorial search}. Rather the true test for such algorithms is whether the empirical scaling curves for these algorithms are more computationally amenable than an existing baseline search algorithm (like CoT).

Three motivating points:

- GEPA / evolutionary agents are performing a zero-th order (no gradient) optimization in a combinatorial space. Their loss curves are VERY noisy and stochastic. If we run such agents multiple times, the performance variance is extremely high -- and in some cases cancels out the gains from single experiment. However, obtaining the error bounds is hard because the API costs are pretty restrictive.

- The problem we face with test time scaling is not that prompt engineering is ineffective/less effective than fine-tuning. It is that fine-tuning _reliably_ increases performance for a model for any subset of tasks and the scaling curves for performance per additional data token are well understood.

- Test time optimization techniques work well on in-distribution problems (e.g. generate and debug this Python code) but fail pretty badly on even slightly out of distribution problems (e.g. generate and debug this Julia code). Compare this to gradient search -- it wouldve been so fascinating and confusing if SGD failed to optimize a CNN image classifier on COCO but worked very well on ImageNet.

How do people feel about this? Does this line up with your viewpoints?