arXiv Machine Learning June 8, 2026 By Sichen Wang, Zhipeng Lu

Depth over Fidelity in Fixed-Budget Noisy Evolution Strategies

Read the original on arXiv Machine Learning →

arXiv:2606. 06555v1 Announce Type: cross Abstract: Noisy evolution strategies under fixed evaluation budgets face a depth-fidelity trade-off: spending evaluations to denoise intra-generation rankings reduces the number of distribution updates the optimizer can execute.

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

reinforcement-learning

arXiv AI

Jul 23

LAARA: Layer-Aware Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning

arXiv:2607. 19391v1 Announce Type: cross Abstract: Low-Rank Adaptation is widely used for parameter-efficient fine-tuning, yet existing methods typically assign the same adapter rank to every transformer layer despite their heterogeneous adaptation requirements.

By Ashutosh Tripathi, Surya Deep Singh, Pranab Sahoo, Sriparna Saha

llms fine-tuning benchmarks

arXiv AI

Jun 8

LoRA-DA: Data-Aware Initialization for Low-Rank Adaptation via Asymptotic Analysis

arXiv:2510. 24561v3 Announce Type: replace-cross Abstract: LoRA has become a widely adopted method for PEFT, and its initialization methods have attracted increasing attention.

By Qingyue Zhang, Chang Chu, Tianren Peng, Qi Li, Xiangyang Luo, Zhihao Jiang, Shao-Lun Huang

fine-tuning benchmarks safety

arXiv Machine Learning

Jul 2

Generative Refinement for Low-Budget Black-Box Optimization

arXiv:2607. 00691v1 Announce Type: new Abstract: Black-box optimization is a fundamental science and engineering tool that makes it possible to optimize objectives without gradient information.

By Edouard R. Dufour, Pascal Fua

arXiv Machine Learning

Jun 30

BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning

arXiv:2606. 29184v1 Announce Type: new Abstract: While Low-rank adaptation (LoRA) enables highly efficient fine-tuning by constraining task-specific updates to fixed low-rank subspaces, this rigid design limits representational flexibility and often results in overconfident predictions and miscalibrated uncertainty, especially in low-data regimes.

By Zhibin Duan, Yuhong Wang, Jiahong Fu, Zongsheng Yue, Bo Chen, Zongben Xu

fine-tuning benchmarks

arXiv Machine Learning

Jul 13

Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards

arXiv:2603. 23086v2 Announce Type: replace Abstract: Autoregressive (AR) models are highly effective for image generation, yet their standard maximum-likelihood estimation training lacks direct optimization for sample quality and diversity.

By Orhun Bugra Baran, Melih Kandemir, Ramazan Gokberk Cinbis

diffusion reinforcement-learning

arXiv Machine Learning

Jun 11

Annealed Entropic Allocation for Ranking and Selection

arXiv:2606. 11347v1 Announce Type: cross Abstract: We propose Annealed Entropic Allocation, an annealed weighted soft-min framework for sequential budget allocation in ranking and selection.

By Xin Fei, Juergen Branke

Related stories

LAARA: Layer-Aware Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning

LoRA-DA: Data-Aware Initialization for Low-Rank Adaptation via Asymptotic Analysis

Generative Refinement for Low-Budget Black-Box Optimization

BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning

Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards

Annealed Entropic Allocation for Ranking and Selection