NVIDIA just trained a 14-billion-parameter AI using evolution, not calculus. Every AI today learns through backpropagation. It computes gradients, adjusts weights, repeats. It works, but it demands precision hardware and enormous GPU clusters. Evolution Strategies offered an alternative. Mutate the model, test it, keep what works. Like biological evolution. The problem was speed. Random mutations on GPUs were painfully slow. EGGROLL fixes this with one trick. It splits huge random matrices into two small ones per mutation. The model mutates, tests, and keeps what works. Hundreds of thousands of mutations run at once. > 100x faster training throughput > 91% speed of pure inference > Pretrains models using only integers > Competitive with backprop on reasoning > Works on non-differentiable systems It pretrained a language model from scratch using zero gradients. It also matched reinforcement learning methods on math reasoning tasks. Everyone kept scaling the calculus to train massive AIs. It turns out, we just needed to evolve.
