arXiv Machine Learning July 14, 2026 By Jiaheng Hu, Zizhao Wang, Peter Stone, Roberto Mart\'in-Mart\'in

Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning

Read the original on arXiv Machine 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.

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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

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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

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Learning Transferable Skills using Goal-Conditioned Bisimulation

arXiv:2610.00676v1 Announce Type: cross Abstract: Unsupervised skill discovery has emerged as a promising approach for leveraging reward-free datasets to pretrain general-purpose policies. However, c...

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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.

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Disentangled Skill Representations for Predictive Human Modeling

The paper introduces Skill Abstraction with Interpretable Latents (SAIL), a method that models human skill as a persistent, multi‑dimensional construct inferred from naturalistic behavior over time. SAIL produces a robust skill embedding that blends expert and novice bases, learns transferable subskills through counterfactual subskill swaps, and supports skill‑informed behavior prediction across various in‑domain contexts. Experiments on racing and baseball demonstrate that SAIL achieves strong predictive performance, improves behaviorally grounded disentanglement compared to baselines, and enhances downstream AI coaching outcomes.

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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

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