{"slug": "quasi-monte-carlo-initialization-for-meta-reinforcement-learning", "title": "Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning", "summary": "A new study from arXiv finds that quasi-Monte Carlo weight initialization improves training convergence in meta-reinforcement learning compared to modern orthogonal defaults (SB3) when extrapolated to similar unseen continuous control environments. The paper, submitted on 21 Jul 2026, shows that QMC meta-priors outperform orthogonal initialization on similar tasks, but orthogonal orientation remains globally superior for dissimilar tasks.", "body_md": "# Computer Science > Machine Learning\n\n[Submitted on 21 Jul 2026]\n\n# Title:Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning\n\n[View PDF](/pdf/2607.21637)\n\n[HTML (experimental)](https://arxiv.org/html/2607.21637v1)\n\nAbstract:This paper explores the efficacy of quasi-Monte Carlo (QMC) weight initialization for meta-reinforcement learning within modern benchmark environments. Various sampling methods are used to bound a population-based search and aggregate an optimal prior from a baseline set of tasks. The QMC meta-priors show improvements in training convergence compared to modern orthogonal (SB3) defaults when extrapolated to similar unseen continuous control environments. In dissimilar tasks, the orthogonal orientation was globally superior for an unbiased search.\n\n### Current browse context:\n\ncs.LG\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/quasi-monte-carlo-initialization-for-meta-reinforcement-learning", "canonical_source": "https://arxiv.org/abs/2607.21637", "published_at": "2026-07-27 04:00:00+00:00", "updated_at": "2026-07-27 04:08:20.757430+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/quasi-monte-carlo-initialization-for-meta-reinforcement-learning", "markdown": "https://wpnews.pro/news/quasi-monte-carlo-initialization-for-meta-reinforcement-learning.md", "text": "https://wpnews.pro/news/quasi-monte-carlo-initialization-for-meta-reinforcement-learning.txt", "jsonld": "https://wpnews.pro/news/quasi-monte-carlo-initialization-for-meta-reinforcement-learning.jsonld"}}