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Source channel @githubtrending · Post #15539 · Mar 5

#python#agent#llm#llm_agent#llm_reasoning#machine_learning_systems#mlsys#reinforcement_learning#rl AReaL is a free, open-source system for fast asynchronous reinforcement learning to train large AI models in math, coding, search, and agents. It decouples generation and training for up to 2.77x speedup, stable performance, and easy setup on single or 1000+ GPUs with algorithms like GRPO/PPO. Install via git/pip, run examples like GSM8K math instantly. You benefit by building top AI agents affordably and quickly, reproducing results with shared data/models, saving time/money vs. slow synchronous tools. https://github.com/inclusionAI/AReaL

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ФГБУН Институт высокотемпературной электрохимии УрО РАН

@ihteubras · Post #113 · 10/20/2022, 07:06 AM

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Новая работа регулярного выпуска👇 🟢 2022 🟢 V. 9 🟢 Issue 4 🟢 No. 20229415🟢 Letter 📜 Local structure and ionic transport in acceptor-doped layered perovskite BaLa2In2O7 👩‍🎓 Nataliia A. Tarasova (https://orcid.org/0000-0001-7800-0172) 🏛 Institute of High Temperature Electrochemistry UB RAS, http://www.ihte.uran.ru 📚#layered_perovskite#ionic#conductivity#acceptor_doping#BaLa2In2O7 🔗https://doi.org/10.15826/chimtech.2022.9.4.15 https://journals.urfu.ru/index.php/chimtech/article/view/6272

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ФГБУН Институт высокотемпературной электрохимии УрО РАН

@ihteubras · Post #7 · 08/04/2022, 10:03 AM

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Новая работа регулярного выпуска👇 🟢 2022 🟢 V. 9 🟢 Issue 4 🟢 No. 20229405🟢 📜 Phosphorus-doped protonic conductors based on BaLanInnO3n+1 (n = 1, 2): applying oxyanion doping strategy to the layered perovskite structure 👩‍🎓👨‍🎓 N. Tarasova (https://orcid.org/0000-0001-7800-0172), A. Galisheva (https://orcid.org/0000-0003-4346-5644) 🏛 Institute of High Temperature Electrochemistry, http://www.ihte.uran.ru 📚#layered#perovskite#oxyanion#doping#proton#conductivity#BaLaInO4#BaLa2In2O7 🔗https://doi.org/10.15826/chimtech.2022.9.4.05 https://journals.urfu.ru/index.php/chimtech/article/view/5979

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