{"slug": "active-curriculum-refinement-for-reinforcement-learning", "title": "Active Curriculum Refinement for Reinforcement Learning", "summary": "Researchers introduced PATH, a curriculum-learning framework for reinforcement learning that actively learns over a directed acyclic curriculum graph, expanding coverage by sampling diverse paths and reallocating training to unmastered regions. Experiments across diverse environments showed PATH leverages graph structure for strong robustness and generalization.", "body_md": "# Computer Science > Machine Learning\n\n[Submitted on 26 Aug 2026]\n\n# Title:Active Curriculum Refinement for Reinforcement Learning\n\n[View PDF](/pdf/2608.26469)\n\n[HTML (experimental)](https://arxiv.org/html/2608.26469v1)\n\nAbstract:In many reinforcement learning (RL) domains, environments are connected by prerequisite relations, such as difficulty-increasing edits or parameter increments, which induce a directed acyclic curriculum graph (DAG). Although this structure is often exploited only implicitly, explicitly modeling it can improve training. We introduce PATH, a curriculum-learning framework that performs active learning over the curriculum graph. PATH first expands coverage by sampling diverse curriculum paths and then reallocates training toward regions that remain unmastered. Experiments across diverse environments show that PATH explicitly leverages the graph structure to achieve strong robustness and generalization.\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/active-curriculum-refinement-for-reinforcement-learning", "canonical_source": "https://arxiv.org/abs/2608.26469", "published_at": "2026-08-28 04:00:00+00:00", "updated_at": "2026-08-28 04:21:17.422718+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["PATH"], "alternates": {"html": "https://wpnews.pro/news/active-curriculum-refinement-for-reinforcement-learning", "markdown": "https://wpnews.pro/news/active-curriculum-refinement-for-reinforcement-learning.md", "text": "https://wpnews.pro/news/active-curriculum-refinement-for-reinforcement-learning.txt", "jsonld": "https://wpnews.pro/news/active-curriculum-refinement-for-reinforcement-learning.jsonld"}}