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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 #15440 · 01/27/2026, 12:00 PM

#go#config#config_loader#configuration#configuration_file#configuration_management#etcd_client#go#golang#golang_package#s3_bucket#toml#viper#yaml koanf is a lightweight Go library to load config from files (JSON, YAML, TOML), env vars, flags, S3, Vault and more, merging them easily with dot-path keys like "app.server.port". Install core with `go get github.com/knadh/koanf/v2`, add providers/parsers as needed. It's a cleaner Viper alternative with fewer dependencies and better extensibility. This saves you time by simplifying config in apps, letting you override values flexibly without bloat or forced orders. https://github.com/knadh/koanf