{"slug": "efficient-exploration-is-enough", "title": "Efficient Exploration Is Enough", "summary": "A new arXiv preprint (submitted September 7, 2026) proposes that efficient exploration in reinforcement learning should prioritize generating generalizable experience, and shows theoretically that optimally efficient explorers schedule trajectories to visit the most informative and learnable regions first, while empirically this intrinsic objective alone can drive an automatic curriculum of progressively more complex behaviors without external rewards.", "body_md": "# Computer Science > Machine Learning\n\n  [Submitted on 7 Sep 2026]\n\n# Title:Efficient Exploration Is Enough\n\n[View PDF](/pdf/2609.07575)\n\n[HTML (experimental)](https://arxiv.org/html/2609.07575v1)\n\nAbstract:This work introduces an alternative view of efficient exploration and studies its theoretical and empirical implications in the absence of extrinsic rewards. Specifically, we define efficient explorers as agents that prioritize generating generalizable experience, i.e., data that supports learning models capable of predicting and adapting across the environment. This allows us to analyze efficient exploration through the lens of prediction and generalization. Theoretically, we demonstrate that optimally efficient explorers naturally schedule their trajectories to visit the most informative and learnable regions first. Empirically, we show that optimizing for these agents gives rise to an automatic curriculum of progressively more complex behaviors, even in relatively simple environments. These results indicate that pursuing this purely intrinsic objective alone is enough to drive the emergence of highly sophisticated behaviors. We believe that this new framework provides a principled mechanism by which agent-environment systems may sustain an open-ended process of increasingly complex behavior without external rewards, tasks, or objectives.\n    \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/))\n# 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))\n# 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))\n# 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/efficient-exploration-is-enough", "canonical_source": "https://arxiv.org/abs/2609.07575", "published_at": "2026-09-09 05:27:28+00:00", "updated_at": "2026-09-09 06:01:02.606625+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/efficient-exploration-is-enough", "markdown": "https://wpnews.pro/news/efficient-exploration-is-enough.md", "text": "https://wpnews.pro/news/efficient-exploration-is-enough.txt", "jsonld": "https://wpnews.pro/news/efficient-exploration-is-enough.jsonld"}}