新的无参数优化方法提高了LLM微调效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-16 04:00

研究人员推出了一种名为AdaNAGED的新型无参数优化方法，旨在高效微调大型语言模型（LLMs）。该方法统一了无梯度训练、自适应参数调整和几何感知更新，解决了传统反向传播方法相关的内存开销问题。该方法已证明了收敛性保证，并在OPT-1.3B模型上对大规模LLM微调任务进行了验证。 AI

影响这项新的优化技术可以显著减少微调大型语言模型所需的计算资源，从而使先进的人工智能更加普及。

排序理由该集群包含一篇关于LLM新优化方法的学术论文，已提交至arXiv。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Dmitriy Bystrov, Daniil Medyakov, Dmitry Bylinkin, Aleksandr Beznosikov · 2026-06-16 04:00

LMO-based 方法的零阶无参数优化：高效微调的新方法

arXiv:2606.14970v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) has become a central application of modern optimization, enabling pretrained models to adapt to diverse downstream tasks and domain-specific data. A major obstacle in large-scale fine-tuning …