A new way to fine-tune LLMs just dropped

Evolution strategies, long considered unscalable for deep neural networks, are making a comeback in LLM fine-tuning. Two key papers are driving this revival: 'Evolution Strategies at Scale' (Sept 2025), which showed ES can fine-tune billion-parameter models using a population of just 30 models by exploiting the low intrinsic dimensionality of useful update directions; and 'EgRoL' (Nov 2025), which structures perturbations as LoRA updates to dramatically reduce compute costs. EgRoL enables massively parallel inference-only training without backpropagation, outperforming GRPO on benchmarks like Countdown (35% vs 23% accuracy) and GSM8K while running up to 32x more parallel generations under the same hardware. The key insight is that ES fits naturally into RL-style fine-tuning where only a coarse outcome-level reward is available, avoiding the sparse credit assignment problem that plagues token-level RL methods like GRPO.