arXiv AI

Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning

arXiv:2504. 17356v3 Announce Type: replace Abstract: Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task.

arXiv Machine Learning

Jul 31

TAPO: Transition-Aware Policy Optimization for LLM Agents

arXiv:2607. 27973v1 Announce Type: new Abstract: Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents.

By Cong Li, Peixi Peng, Yisen Zhao, Xinyu Hu, Shudong Liu, Zhan Su, Zhuojian Li

llms agents reinforcement-learning

arXiv AI

Aug 5

Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks

arXiv:2608. 03502v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents.

By Christophe D. Hounwanou, John Emeka Eze, Ya\'e Ulrich Gaba

llms agents reinforcement-learning

arXiv AI

Jul 3

BuilderBench: The Building Blocks of Intelligent Agents

arXiv:2510. 06288v4 Announce Type: replace Abstract: Today's AI models learn primarily through mimicry and refining, so it is not surprising that they struggle to solve problems beyond the limits set by existing data.

By Raj Ghugare, Roger Creus Castanyer, Catherine Ji, Kathryn Wantlin, Jin Schofield, Karthik Narasimhan, Benjamin Eysenbach

llms agents reinforcement-learning robotics benchmarks

arXiv AI

5d ago

AHD Agent: Agentic Reinforcement Learning for Automatic Heuristic Design

arXiv:2605. 08756v2 Announce Type: replace Abstract: Automatic heuristic design (AHD) has emerged as a promising paradigm for solving NP-hard combinatorial optimization problems (COPs).

By Haoze Lv, Ning Lu, Ziang Zhou, Yew-Soon Ong, Shengcai Liu

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 26

Joint Learning of Experiential Rules and Policies for Large Language Model Agents

arXiv:2606. 27136v1 Announce Type: new Abstract: For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience.

By Shicheng Ye, Chao Yu

llms agents

arXiv AI

Jul 29

Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering

arXiv:2607. 25090v1 Announce Type: new Abstract: Machine learning engineering (MLE) tasks require long-horizon decision making over iterative solution debugging and refinement, under expensive and feedback-driven environment interactions.

By Rushi Qiang, Changhao Li, Haotian Sun, Yuchen Zhuang, Chao Zhang, Bo Dai

agents

arXiv AI

Jun 10

HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning

arXiv:2606. 10507v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents across a wide range of tasks, their performance often degrades in multi-turn long-horizon agentic tasks.

By Juncheng Diao, Zhicong Lu, Peiguang Li, Yongwei Zhou, Changyuan Tian, Qingbin Li, Rongxiang Weng, Jingang Wang, Xunliang Cai

llms agents nlp reinforcement-learning benchmarks

arXiv Machine Learning

Jun 3

Multi$^2$: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments

arXiv:2606. 03698v1 Announce Type: new Abstract: A central goal of large language model (LLM) research is to build agentic systems that can plan, act, and adapt through sustained interaction with dynamic environments.

By Sangeun Park, Minhae Kwon

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 3

Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning

arXiv:2606. 03762v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) equips large language models (LLMs) with tool-use capabilities that substantially improve reasoning on complex tasks.

By Hongye Cao, Nuo Yan, Haoyuan Deng, Ziwei Wang, Tianpei Yang, Jing Huo, Yuyao Zhang, Yang Gao

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 7

A Technical Survey of Reinforcement Learning Techniques for Large Language Models

arXiv:2507. 04136v2 Announce Type: replace Abstract: This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Policy Optimization (PPO), Q-Learning, and Actor-Critic methods.

By Saksham Sahai Srivastava, Vaneet Aggarwal

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 17

EnvRL: Learn from Environment Dynamics in Agentic Reinforcement Learning

arXiv:2606. 17680v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for training Large Language Models (LLMs) as agents.

By Zhitong Wang, Songze Li, Hao Peng, Shuzheng Si, Yi Wang, Maosong Sun, Juanzi Li

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 7

ICR-RL: Deep Reinforcement Learning via In-Context Regression

arXiv:2509. 11259v2 Announce Type: replace-cross Abstract: Recent advancements in machine learning have largely been driven by foundation models (FMs) trained on large, diverse datasets, enabling them to generalize effectively to new, related tasks.

By David Schiff, Ofir Lindenbaum, Yonathan Efroni

agents reinforcement-learning fine-tuning benchmarks

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