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SLIME is a distributed reinforcement learning framework for large language model post-training that bridges Megatron training with SGLang inference servers. It orchestrates scalable RL loops across GPU clusters, decoupling training and inference into independent processes that communicate over HTTP and NCCL for independent scaling and fault tolerance. The system supports multi-agent reinforcement learning workflows with parallel agent instances, customizable rollout strategies, and personalized agent serving that improves models from prior conversations without disrupting API serving. The fra
This library provides a comprehensive framework for fine-tuning, aligning, and distilling transformer-based language models. It serves as a toolkit for adapting models to specialized domains through supervised learning, while offering advanced methodologies to improve output quality and reasoning capabilities. The project distinguishes itself through specialized alignment and optimization techniques, including direct preference optimization and reinforcement learning, which allow models to be tuned against human preferences without complex reward modeling. It further supports training efficie
OpenRLHF is a training framework and alignment library designed for reinforcement learning from human feedback across distributed GPU clusters. It provides tools for aligning large language models and multimodal vision-language models using algorithms such as PPO, GRPO, and DPO. The framework distinguishes itself through a distributed inference engine that overlaps sample rollout with training to increase throughput. It supports scaling to models exceeding 70 billion parameters via parameter sharding and handles long-context sequences through ring-attention sequence parallelism. The project
Habitat-Lab is an open-source platform for training and evaluating embodied AI agents in photorealistic 3D indoor environments. It functions as a high-performance 3D indoor environment simulator that supports physics-based interaction, enabling research into navigation and manipulation tasks. The platform provides a modular task-environment abstraction that separates task logic from environment simulation, using configuration-driven pipeline assembly to compose simulation and training pipelines. It includes a hierarchical sensor-actuator architecture for mixing and matching perception and act
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The main features of nvidia-nemo/rl are: Reinforcement Learning Frameworks, Training and Fine-Tuning.
Projects with overlapping indexed features include: openrlhf/openrlhf — OpenRLHF is a training framework and alignment library designed for reinforcement learning from human feedback across… thudm/slime — SLIME is a distributed reinforcement learning framework for large language model post-training that bridges Megatron… huggingface/trl — This library provides a comprehensive framework for fine-tuning, aligning, and distilling transformer-based language… tflearn/tflearn — tflearn is a deep learning framework and high-level API wrapper for TensorFlow. It provides a toolkit for designing… google/dopamine — Dopamine is a reinforcement learning research framework designed for prototyping and testing algorithms across diverse… facebookresearch/habitat-lab — Habitat-Lab is an open-source platform for training and evaluating embodied AI agents in photorealistic 3D indoor…