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Source channel @githubtrending · Post #15340 · Dec 17

#python#gym#gym_environment#reinforcement_learning#reinforcement_learning_agent#reinforcement_learning_environments#rl_environment#rl_training NeMo Gym helps you build and run reinforcement‑learning training environments for large language models, letting you develop, test, and collect verified rollouts separately from the training loop and integrate with your preferred RL framework and model endpoints (OpenAI, vLLM, etc.). It includes ready resource servers, datasets, and patterns for multi‑step, multi‑turn, and tool‑using scenarios, runs on a typical dev machine (no GPU required), and is early-stage with evolving APIs and docs. Benefit: you can generate high‑quality, verifiable training data faster and plug it into existing training pipelines to improve model behavior. https://github.com/NVIDIA-NeMo/Gym

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ФГБУН Институт высокотемпературной электрохимии УрО РАН

@ihteubras · Post #557 · 05/07/2024, 10:05 AM

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Опубликована новая статья 🟣 2024 🟣 V. 11 🟣 Issue 2 🟣 Art. 202411207 🟣 Letter 🟣 📜 Features of electronic states in the vicinity of band gap and atomic structure of Ta- and Nb-doped Li7La3Zr2O12 👩‍🎓👨‍🎓 M. I. Vlasov (http://orcid.org/0000-0002-7814-7489), E.A. Surzhikov (http://orcid.org/0009-0005-3466-6374), A.Yu. Germov (http://orcid.org/0000-0001-6091-1250), E.A. Il'ina (http://orcid.org/0000-0003-1759-5234), I.A. Weinstein (http://orcid.org/0000-0002-5573-7128) 🏛 Institute of High Temperature Electrochemistry of the Ural Branch of the Russian Academy of Sciences, https://ihte.ru/?page_id=3106 🏛 Ural Federal University, https://urfu.ru/en 🏛 M.N. Mikheev Institute of Metal Physics of the Ural Branch of the Russian Academy of Sciences, https://www.imp.uran.ru/?q=en 🏛 Institute of Metallurgy of the Ural Branch of the Russian Academy of Sciences, http://www.imet-uran.ru 📚#Li7La3Zr2O12#Ta#Nb#doping#bandgap#oxygen#vacancies 🔗https://doi.org/10.15826/chimtech.2024.11.2.07 https://journals.urfu.ru/index.php/chimtech/article/view/7692

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