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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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RCNN Ticker

@RCNNticker · Post #315 · 12/19/2017, 04:21 PM

全球第五个单人六重蓝竹笋 也是 呆瓜 @DuangWB (原 @DuangW) 的第二个单六 于 12 月 10 日 清晨 06:49 (中国 04:49) 耗时 5h18min 再次在澳大利亚布里斯班完成 距第四个单六竹笋被呆瓜于 11 月 26 日 耗时 8h48min 完成 仅过两周 #Homogeneous

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В журнале Electrochemical Materials and Technologies вышла обзорная работа "H/D exchange studies of methane activation mechanisms in heterogeneous catalysis" 🔗https://doi.org/10.15826/elmattech.2023.2.014 🔗https://journals.urfu.ru/index.php/elmattech/article/view/6883 В данном обзоре подробно рассматривается механизм конверсии метана и анализируются существующие теоретические и экспериментальные подходы к изотопному обмену H/D между метаном и каталитическими системами: #CH4#methane#conversion#isotope#catalyst#bonds#homogeneous#exchange #