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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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#python #gym #gym_environment #reinforcement_learning #reinforcement_learning_agent #reinforcement_learning_environments #rl_environment #rl_training

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@githubtrending · Post #15246 · 10/24/2025, 01:30 PM

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#go#blob_storage#cloud_drive#distributed_file_system#distributed_storage#distributed_systems#erasure_coding#fuse#hadoop_hdfs#hdfs#kubernetes#object_storage#posix#replication#s3#s3_storage#seaweedfs#tiered_file_system SeaweedFS is a fast, simple, and highly scalable distributed file system designed to store billions of files and serve them quickly, especially small files. It uses a master server to manage volumes on volume servers, which handle file data and metadata, enabling very fast file access with minimal disk reads. It supports features like replication, erasure coding, cloud integration for elastic storage, and compatibility with many metadata stores and APIs including Amazon S3. This means you get efficient, cost-effective storage with fast access, easy scaling, and flexible deployment options for large-scale file storage needs. https://github.com/seaweedfs/seaweedfs

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