arXiv AI June 15, 2026 By Yinggan Xu, Kajetan Schweighofer, Risto Miikkulainen, Xin Qiu

Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost

arXiv:2602. 03120v2 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory-constrained devices, yet it renders models static and difficult to fine-tune.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Sep 1

Fine-Tuning Low-Bit Models with Gradient in Quantized Code Space

arXiv:2608.30908v1 Announce Type: new Abstract: Fine-tuning Low-bit models aims to adapt a quantized model while keeping the final deployed checkpoint in the same low-bit form. This setting is practi...

By Shiguang Wu, Zhouchen Lin, Quanming Yao

fine-tuning efficiency

arXiv Machine Learning

Jun 2

WINDQuant: Weight-Informed Neural Decision-Making for Global Mixed-Precision LLM Quantization

arXiv:2605. 26660v2 Announce Type: replace Abstract: Quantization is an effective approach to reduce the memory footprint and inference cost of large language models (LLMs), yet maintaining performance in the ultra-low-bit regime remains challenging.

By Phong Nam Huu Nguyen, Khoi M. Le, Cong-Duy T Nguyen, Anh Tuan Luu, Thong Thanh Nguyen, Tho Quan

llms reinforcement-learning efficiency

arXiv AI

Jul 15

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

arXiv:2509. 24372v3 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) for downstream tasks is an essential stage of modern AI deployment.

By Xin Qiu, Yulu Gan, Conor F. Hayes, Qiyao Liang, Yinggan Xu, Roberto Dailey, Elliot Meyerson, Babak Hodjat, Risto Miikkulainen

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Sep 22

Towards Training-free Automatic Proxy Discovery via Large Language Models for Mixed Precision Quantization

arXiv:2512.07419v3 Announce Type: replace Abstract: Mixed-Precision Quantization (MPQ) liberates Deep Neural Networks (DNNs) from the Out-Of-Memory (OOM) bottleneck and has garnered increasing resear...

By Haidong Kang, Jun Du, Guo Yu

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Aug 10

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization

arXiv:2608. 07019v1 Announce Type: new Abstract: Post-training quantization (PTQ) is widely used to reduce the memory and computational cost of large language models.

By Yongge Ma, Guoan Wang, Feiyu Wang, Yaoming Li, Qian Zhang, Zihan Yan, Yinjun Han, Tong Yang

llms efficiency

arXiv AI

Sep 2

REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent

REAL-Q introduces a new post‑training quantization approach for large language models that replaces the traditional single closed‑form second‑order solver with a fine‑grained, dynamic block‑wise gradient descent applied after every 128‑column block. By aligning the surrogate loss with the end‑to‑end objective and using a sliding window for smooth cross‑layer transitions, REAL‑Q mitigates error propagation and information misalignment. Experiments on LLaMA‑3.1 and Qwen3 show up to ~49% reduction in end‑to‑end KL divergence compared to state‑of‑the‑art methods.

By Qian Zhang, Yaoming Li, Zhewen Tan, Yanshu Wang, Heng Lu, Kun Su, Zongwei Lv, Wenhan Yu, Yongge Ma, Yinjun Han, Ruikuang Liu, Tong Yang

llms efficiency benchmarks

One email a morning, machine-written

Related stories

Fine-Tuning Low-Bit Models with Gradient in Quantized Code Space

WINDQuant: Weight-Informed Neural Decision-Making for Global Mixed-Precision LLM Quantization

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

Towards Training-free Automatic Proxy Discovery via Large Language Models for Mixed Precision Quantization

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization

REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent