Hugging Face Trending Papers August 6, 2026

Relay, Don't Route: Adaptive Population Handoff for Cost-Efficient LLM-Driven Evolution

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Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long evolutionary runs is costly. A natural alternative is to combine cheap and strong models under a fixed inference budget.

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arXiv AI

Aug 7

Relay, Don't Route: Adaptive Population Handoff for Cost-Efficient LLM-Driven Evolution

arXiv:2608. 05651v1 Announce Type: cross Abstract: Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long evolutionary runs is costly.

By Sichun Luo, Yi Huang, Guanzhi Deng, Haibo Wang, Haochen Luo, Lei Li, Zefa Hu, Junlan Feng, Qi Liu

llms reinforcement-learning benchmarks

arXiv AI

Sep 15

Compute Allocation for Self-Evolving LLMs: From Depth-Breadth to Multi-Armed Bandits

arXiv:2605.29268v3 Announce Type: replace-cross Abstract: LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existi...

By Sixue Xing, Haoyu He, Kerui Wu, Zhuo Yang, Haozheng Luo, Tianfan Fu, Aarthy Nagarajan

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 30

Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks

arXiv:2606. 29082v1 Announce Type: cross Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture?

By Young-Jun Lee, Seungone Kim, Minki Kang, Alistair Cheong Liang Chuen, Zerui Chen, Seungho Han, Taehee Jung, Dongyeop Kang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 28

WISERouter: LLM Routing with Workload Budget Constraint

arXiv:2607. 23765v1 Announce Type: cross Abstract: Large language models (LLMs) achieve impressive performance across multiple domains, but using the most capable model for every query is prohibitive at scale.

By Yifei Li, Zihui Gao, Laks V. S. Lakshmanan

llms reinforcement-learning

arXiv Machine Learning

Sep 2

ES-AHD: An Evolution Strategy Framework for Automatic Heuristic Design

arXiv:2609.00023v1 Announce Type: cross Abstract: In this paper, we introduce ES-AHD, a novel framework that fundamentally integrates Evolution Strategy (ES) into Large Language Model (LLM)-driven Au...

By Yutao Lai, Kezhao Lai, Hai-Lin Liu, Yuping Wang, Ping Guo

llms rag

arXiv AI

Sep 12

COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization

COBRA‑Skills is a new framework that treats skill optimization for large language model agents as a budgeted sequential problem over a dynamically evolving candidate set. It uses contextual‑bandit prioritization to focus evaluations on promising or informative candidates and refines the skill population based on execution feedback. In experiments across six agent benchmarks and three target models, COBRA‑Skills outperforms existing methods, cuts optimization cost by 55–58 % compared to SkillOpt, and requires only 50 unique optimization examples per benchmark.

By Pingchen Lu, Xiangyi Wang, Xiang Li, Jie Mao, Zikun Qu, Junfeng Luo, Yao Shu, Bryan Kian Hsiang Low, Zhongxiang Dai

llms agents reinforcement-learning benchmarks

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Relay, Don't Route: Adaptive Population Handoff for Cost-Efficient LLM-Driven Evolution

Compute Allocation for Self-Evolving LLMs: From Depth-Breadth to Multi-Armed Bandits

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