# Evolution strategies match reinforcement learning for billion-parameter LLM fine-tuning, arXiv preprint finds

_Wednesday, July 15, 2026 at 4:07 AM EDT · Science, AI · Latest · Tier 2 — Notable_

An arXiv preprint demonstrates the first successful application of evolution strategies (ES) to full-parameter fine-tuning of large language models at the billion-parameter scale without dimensionality reduction. The paper challenges the widespread assumption that ES does not scale to modern model sizes. The authors show that ES can search over extremely high-dimensional parameter spaces and outperforms established reinforcement learning (RL) implementations across multiple axes, including improved tolerance to long-horizon and delayed rewards, robustness across diverse base LLMs, reduced susceptibility to reward hacking, and improved training stability. The findings suggest ES is not merely a viable alternative to RL but a fundamentally different and powerful backpropagation-free post-training paradigm that opens a new direction for LLM fine-tuning beyond current RL-based approaches. RL has become the dominant fine-tuning paradigm underpinning many state-of-the-art LLMs, while ES has been largely overlooked due to scaling concerns. This work overturns that assumption by demonstrating ES effectiveness at billion-parameter scale for the first time.

## Sources

- [cs.AI updates on arXiv.org](https://arxiv.org/abs/2509.24372)

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Retrieved: 2026-07-15T12:01:04.214Z
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