LLM Post-Training & RLHF Ecosystem

The Reinforcement Learning (RL) landscape is split into two major ecosystems: Classical/Traditional RL (used for robotics, games, and continuous control) and LLM Post-Training / RLHF (used for aligning and training reasoning models like O1 or DeepSeek with GRPO). 

Here is a comprehensive list of the major open-source RL frameworks grouped by their design intent:

1. LLM Post-Training & RLHF Ecosystem

These frameworks are highly optimized for distributed cluster training, decoupling fast token generation (rollouts) from model optimization.

• Verl (VeRL): A high-performance, flexible RL stack by ByteDance optimizing model placement and prefix-caching.
• TRL (Transformer Reinforcement Learning): Hugging Face’s popular library supporting SFT, DPO, PPO, and GRPO. Frequently paired with Unsloth RL for memory optimization.
• OpenRLHF: An early, widely adopted distributed framework supporting Ray, Megatron-LM, and DeepSpeed.
• AReaL: An asynchronous framework explicitly designed to scale up hardware throughput by splitting actor and learner resources.
• Slime: An SGLang-native RL framework integrating SGLang inference with Megatron training.
• NeMo-RL / NeMo Aligner: NVIDIA's massive-scale alignment framework tailored for NeMo-based LLMs.
• ROLL: Alibaba’s framework focusing on multi-turn agentic conversations and complex reasoning paths.
• RAGEN: An extension framework built on top of Verl, specifically targeted at agentic training and multi-turn environments. 

2. General-Purpose & Scalable Production Deep RL

Built to handle traditional deep learning architectures for high-throughput and enterprise deployment.

• Ray RLlib: The dominant framework for distributed computing, natively handling multi-agent workflows and fault-tolerant scaling.
• Stable-Baselines3 (SB3): The gold standard for PyTorch-based implementations, known for its reliable, plug-and-play code structure.
• Tianshou: A highly parallelized, modular PyTorch library that stands out for its high performance and native multi-agent support.
• CleanRL: A lightweight library specializing in single-file implementations of algorithms, making it perfect for rapid prototyping and educational tweaks.
• TorchRL: PyTorch's official modular foundation library that serves as the base for building low-level custom RL components. 

3. Big Tech Research Frameworks

Frameworks released by core research labs to publish and validate new theoretical algorithms.

• Acme: DeepMind's flagship library structured around building actor-critic architectures and testing across environments.
• TF-Agents: Google's structured library designed exclusively for native TensorFlow model integration.
• Dopamine: Google’s minimalist research framework designed primarily for fast prototyping in Atari-like environments.
• ReAgent: Meta’s discrete and continuous control platform tailored specifically for real-world production decision systems. 

4. Environments & Toolkits (The Ecosystem Glue)

While not training engines themselves, these provide standard interfaces or simulations required to run any RL algorithm. 

• Gymnasium: The actively maintained successor to OpenAI’s original Gym, serving as the standard environment API standard.
• Unity ML-Agents: The standard bridge allowing games and simulations built inside the Unity Engine to behave as RL environments.
• HUD / Harbor: Frameworks optimized specifically for sandboxing and validating AI code agents against containerized terminals.