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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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@githubtrending · Post #14919 · 07/06/2025, 12:00 PM

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#cplusplus#aes#avx#avx_instructions#chrome#chrome_devtools#chromedriver#chromium#chromium_browser#content_shell#jpeg_xl#jpegxl#jxl#libjxl#linux#thorium#thorium_browser#thoriumos#web_browser#web_platform#webbrowser Thorium is a fast, optimized web browser based on Chromium, designed to work well on modern CPUs with advanced instruction sets like AVX and SSE4. It offers better performance than standard Chromium and Chrome, opening tabs and rendering pages quickly. Thorium includes enhanced privacy features such as DNS over HTTPS and Do Not Track enabled by default, plus support for modern media formats like HEVC and JPEG XL. It keeps the familiar Chrome interface and supports all Chrome extensions, making it easy to switch. Available on Windows, Linux, macOS, Android, and Raspberry Pi, it suits users wanting speed, privacy, and compatibility across devices[3][5][1]. https://github.com/Alex313031/thorium