arXiv Machine Learning

Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning

arXiv:2410. 11251v2 Announce Type: replace Abstract: A hallmark of intelligent agents is the ability to learn reusable skills purely from unsupervised interaction with the environment.

arXiv AI

Jun 4

SUSD: Structured Unsupervised Skill Discovery through State Factorization

arXiv:2602. 01619v2 Announce Type: replace-cross Abstract: Unsupervised Skill Discovery (USD) aims to autonomously learn a diverse set of skills without relying on extrinsic rewards.

By Seyed Mohammad Hadi Hosseini, Mahdieh Soleymani Baghshah

agents reinforcement-learning

arXiv Machine Learning

Jul 30

SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution

arXiv:2607. 26784v1 Announce Type: new Abstract: Large language model agents often encounter related yet distinct tasks that share reusable solution patterns.

By Zhiyuan Yao, Yuxin Chen, Zhengxi Lu, Zishan Xu, Yueqing Sun, Yifu Guo, Yuquan Lu, Zhengzhou Cai, Kangning Zhang, Zhuowen Han, Zi-Han Wang, Ziang Ye, Qi Gu, Xunliang Cai, Weiwen Liu, Yongliang Shen

llms agents reinforcement-learning

Hugging Face Trending Papers

Jul 29

SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution

Large language model agents often encounter related yet distinct tasks that share reusable solution patterns. Yet standard agentic reinforcement learning treats tasks as independent episodes, while existing approaches to skill learning either focus on repeated attempts of one task or use pipelines with multiple stages that entangle extraction, retrieval, and execution.

llms agents reinforcement-learning

arXiv AI

Jul 2

Learning Generalizable Skill Policy with Data-Efficient Unsupervised RL

arXiv:2607. 00392v1 Announce Type: cross Abstract: Unsupervised Reinforcement Learning (URL) aims to pre-train scalable, skill-conditioned policies without extrinsic rewards, serving as a foundation for downstream control tasks.

By Jongchan Park, Seungjun Oh, Seungho Baek, Yusung Kim

agents reinforcement-learning

arXiv Machine Learning

Jul 22

HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents

arXiv:2508. 14751v2 Announce Type: replace Abstract: We study goal-conditioned reinforcement learning in partially observable environments with sparse rewards and large, structured goal spaces.

By Thomas Carta, Cl\'ement Romac, Loris Gaven, Pierre-Yves Oudeyer, Olivier Sigaud, Sylvain Lamprier

llms agents reinforcement-learning

arXiv AI

Aug 11

Bidirectional Context Self-Distillation for Reinforcement Learning of Skill-Based LLM Agents

arXiv:2608. 09555v1 Announce Type: new Abstract: External natural-language skills provide large language model (LLM) agents with reusable and editable guidance for solving complex tasks.

By Tianjun Pan, Yuan Li, Hongda Wang, Linbo Jin, Mengfei Song, Lei Gao, Qiming Shi, Shaokang Fu, Jiarong Zhao, Chengyu Wang, Chengfu Huo

llms agents reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jun 2

World-Task Factorization for Robot Learning

arXiv:2606. 02027v1 Announce Type: cross Abstract: Robot learning must produce policies that generalize to new combinations of constraints, teammates, and environments.

By Eduardo Sebasti\'an, Adrian Pfisterer, Vito Mengers, Oliver Brock, Amanda Prorok

robotics

arXiv AI

Aug 5

TurnSight: Turn-Level Hindsight Self-Distillation for Tool-Integrated Reasoning

arXiv:2608. 04007v1 Announce Type: cross Abstract: Tool-Integrated Reasoning (TIR) enables LLMs to solve complex tasks through iterative tool interactions.

By Changle Qu, Sunhao Dai, Hengyi Cai, Yuqi Zhou, Xinran Chen, Simon, Jun Xu

llms agents reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Aug 12

BooST: Bridging Semantics and Motions for Efficient Skill Transfer

arXiv:2608. 10600v1 Announce Type: cross Abstract: Skill abstraction---the process of learning reusable and temporally extended behaviors---has emerged as a key paradigm for improving sample efficiency and generalization in robot learning.

By Jusuk Lee, Daesol Cho, Jonghun Shin, Seungyeon Yoo, Jonghae Park, Taekbeom Lee, H. Jin Kim

robotics multimodal

arXiv Machine Learning

Jun 18

Do as the Romans Do: Learning Universal Behaviors from Heterogeneous Agents

arXiv:2606. 18537v1 Announce Type: new Abstract: Humans often acquire new skills by observing others, since observed behaviors implicitly reveal how to act in an environment.

By Caleb Chang, Davin Win Kyi, Natasha Jaques, Karen Leung

agents robotics fine-tuning safety

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 AI

Jul 20

SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training

arXiv:2506. 04147v5 Announce Type: replace-cross Abstract: Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom (DoF) systems such as mobile manipulators.

By Jiaheng Hu, Peter Stone, Roberto Mart\'in-Mart\'in

reinforcement-learning robotics benchmarks safety

Related stories