arXiv AI

IDEA: Insensitive to Dynamics Mismatch via Effect Alignment for Sim-to-Real Transfer in Multi-Agent Control

arXiv:2606. 26575v1 Announce Type: cross Abstract: Complex multi-agent control tasks remain challenging for traditional rule-based and model-based approaches, motivating the adoption of learning-based methods.

arXiv Machine Learning

Jun 5

Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning

arXiv:2512. 09706v2 Announce Type: replace Abstract: The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models.

By Kaichen He, Zihao Wang, Muyao Li, Anji Liu, Yitao Liang

agents reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jun 24

ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning

arXiv:2606. 24601v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) addresses the problem of training multiple agents that pursue collaborative, competitive, or mixed objectives.

By Anurag Akula, Satheesh K. Perepu, Abhishek Sarkar, Kaushik Dey

rag agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 17

Knowledge Reutilization in Meta-Reinforcement Learning

arXiv:2606. 18132v1 Announce Type: new Abstract: Meta-reinforcement learning enables fast adaptation by extracting shared structure from related tasks, but existing end-to-end methods often couple task inference with embodiment-specific control.

By Yuan Meng, Bo Wang, Juan de los Rios Ruiz, Xiangtong Yao, Zhenshan Bing, Fuchun Sun, Alois Knoll

agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Aug 6

State2State: Environment-Derived Mid-Training for LLM Agents

arXiv:2608. 04934v1 Announce Type: cross Abstract: Training LLM agents commonly relies on supervised fine-tuning from expert trajectories or online reinforcement learning over human-specified tasks with handcrafted verifiers.

By Xuanyu Lei, Yiqi Zhu, Chenliang Li, Kaiming Liu, Peng Li, Ming Yan, Jieping Ye, Ya-Qin Zhang, Yang Liu

llms agents reinforcement-learning robotics fine-tuning

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 9

In-Context Reinforcement Learning via Communicative World Models

arXiv:2508. 06659v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) agents often struggle to generalize to new tasks and contexts without updating their parameters, mainly because their learned representations and policies are overfit to the specifics of their training environments.

By Fernando Martinez-Lopez, Tao Li, Yingdong Lu, Juntao Chen

agents reinforcement-learning

arXiv Machine Learning

Jun 4

Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling

arXiv:2606. 05021v1 Announce Type: new Abstract: We investigate multi-agent deep reinforcement learning and propose two enhancements to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm.

By Marc Walden, Jason Liu, Shaashwath Sivakumar, Ryan Liu, Hamza Khan

agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 25

GCT-MARL: Graph-Based Contrastive Transfer for Sample-Efficient Cooperative Multi-Agent Reinforcement Learning

arXiv:2606. 25073v1 Announce Type: new Abstract: In cooperative multi-agent reinforcement learning (MARL), from a deployment perspective, it is challenging and expensive to train agents from scratch for each new environment or task.

By Animesh Animesh, Satheesh K Perepu, Kaushik Dey

agents reinforcement-learning fine-tuning safety

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

Hugging Face Trending Papers

Aug 5

State2State: Environment-Derived Mid-Training for LLM Agents

Training LLM agents commonly relies on supervised fine-tuning from expert trajectories or online reinforcement learning over human-specified tasks with handcrafted verifiers. Though effective, both remain bottlenecked by externally specified tasks and supervision signals, limiting the scalability and diversity of agent training.

llms agents reinforcement-learning robotics fine-tuning

arXiv AI

Jul 7

ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning

arXiv:2602. 21534v3 Announce Type: replace Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks.

By Xiaoxuan Wang, Han Zhang, Haixin Wang, Yidan Shi, Ruoyan Li, Kaiqiao Han, Chenyi Tong, Haoran Deng, Renliang Sun, Alexander Taylor, Yanqiao Zhu, Jason Cong, Yizhou Sun, Wei Wang

llms agents reinforcement-learning

arXiv Machine Learning

Jul 3

Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning

arXiv:2607. 02037v1 Announce Type: cross Abstract: Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment.

By Ruiheng Jiang, Thomas Bi, Raffaello D'Andrea, Aswin Ramachandran

agents reinforcement-learning fine-tuning

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