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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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 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 Machine Learning

Jul 3

QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs

arXiv:2602. 10431v4 Announce Type: replace Abstract: Large language models (LLMs) demand substantial computational and memory resources, posing challenges for efficient deployment.

By Kanghyun Noh, Jinheon Choi, Yulhwa Kim

llms fine-tuning efficiency benchmarks

arXiv AI

Jun 8

Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates

arXiv:2601. 18510v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents excel at general tasks, they inherently struggle with continual adaptation due to the frozen weights after deployment.

By Yibo Li, Zijie Lin, Ailin Deng, Xuan Zhang, Yufei He, Shuo Ji, Tri Cao, Bryan Hooi

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

3d ago

QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstruction

arXiv:2608. 13966v1 Announce Type: new Abstract: As large language model inference shifts toward lower precision, post-training quantization (PTQ) becomes increasingly brittle, making quantization-aware training (QAT) essential for preserving model quality.

By Vincent Counathe, Ben Athiwaratkun, Christopher De Sa, Tianyi Zhang

llms efficiency

Related stories

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

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

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

QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs

Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates

QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstruction