Evolution Is Back: A New Way to Fine‑Tune LLMs If you grew up around game AIs and coding forums, you've probably heard this idea: "One day we'll train superhuman AI with evolution or genetic algorithms." Then deep learning took over, gradient descent won, and evolution‑style methods quietly got pushed into the museum. Now they're back. Evolution Strategies (ES) are being rediscovered as a serious way to fine‑tune large language models (LLMs), building on three key papers: Evolution Strategies as a Scalable Alternative to Reinforcement Learning (OpenAI, 2017) Evolution Strategies at Scale: LLM Fine‑Tuning Beyond Reinforcement Learning (2025) Evolution Strategies at the Hyperscale (EGGROLL) (2025–26) Let's unpack the core ideas in plain language. First: what are evolution strategies, really? Forget math for a second. Think of ES like this: You start with one model. You make a bunch of slightly different copies by adding small random tweaks to its weights.…