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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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Architectural Shovel / Любовь Дмитриева

@arch_shovel · Post #663 · 09/07/2022, 08:31 PM

C U R V E S / Under Magnitude by Marc Fornes / THEVERYMANY The strength of #undermagnitude is achieved by 'Intensive Curvature,' which is the maximization of double #curvature across the project while constraining maximum radii. The result is a #structure that has much tighter curvature with constant change of direction, and results in more structurally performance. 'Intensive Curvature' leads to the curly, tubular branching characteristics consistent across the studio's body of work. In order to achieve structural #stability , each stripe assumes high degrees of curvature individually and high degrees of double curvature in accumulation -- amounting to extreme structural #rigidity throughout the project. #arch_shovel#archdaily