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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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djangoproject

@djangoproject · Post #254 · 02/02/2017, 06:31 PM

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https://groups.google.com/forum/#!topic/pyside-dev/pqwzngAGLWE Dear #Pyside2 contributors, As you might know, Pyside was originally developed for Nokia while it was the owner of the #Qt technology. When Nokia sold Qt to Digia (and now The Qt Company), all the copyrights over the original Pyside code for Qt 4 got transferred to The Qt Company as well. For different reasons, it was not possible for The Qt Company to push Pyside forward as much as we would have wished over the last few years. Fortunately this changed now, and The Qt Company is today in a position, where it can and will invest into Pyside. The goal is to ensure Pyside becomes a fully supported part of the Qt product family, with a similar development and licensing model as the rest of Qt. We want to make sure Pyside works on new Qt releases when they come out and are committed to invest long term into the technology.

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