A flexible and efficient reinforcement learning framework for large language models(LLMs).

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Latest News 🔥

• [2025/9] Support for Agentic RL tasks. documentation 🔥
• [2025/9] Support for Vision-Language model RL tasks. documentation 🔥
• [2025/8] We support GSPO on Mcore! 🔥
• [2025/7] We give a reinforcement learning training example for DeepSeek-V3-671B based on Mcore! 🔥
• [2025/7] We give reinforcement learning training examples for Qwen3-235B-A22B based on Mcore and FSDP2!
• [2025/7] Training now supports the FSDP2 framework! We support sequence packing, sequence parallelism, and group GEMM for efficient and user-friendly reinforcement learning training!
• [2025/5] We support Mcore frameworks for training! By using Mcore and vLLM, we give a tutorial about end-2-end GRPO training for Qwen3!
• [2025/5] We support FSDP frameworks for training! By using FSDP and vLLM, we give a tutorial about end-2-end GRPO training for Qwen3!
• [2024/8] We officially released ChatLearn! Check out our documentation.

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ChatLearn is a large-scale reinforcement learning training framework for LLMs developed by the Alibaba Cloud PAI platform.

Chatlearn has the following advantages:

1. 🚀User-friendly programming interface: Users can focus on programming individual models by wrapping a few functions, while the system takes care of resource scheduling, data and control flow transmission, and distributed execution.
2. 🔧Highly Scalable Training Methodology: ChatLearn supports user-defined model execution flows, making customized training processes more flexible and convenient.
3. 🔄Diverse Distributed Acceleration Engines: ChatLearn supports industry-leading SOTA training (FSDP2, Megatron) and inference engines (vLLM, SGLang), delivering exceptional training throughput performance.
4. 🎯Flexible Parallel Strategies and Resource Allocation: ChatLearn supports different parallel strategies for various model configurations, enabling the formulation of distinct parallel approaches tailored to each model's computational, memory, and communication characteristics. Additionally, ChatLearn features a flexible resource scheduling mechanism that accommodates exclusive or shared use of resources across models. Through its system scheduling policies, it facilitates efficient serial/parallel execution and optimized GPU memory sharing, enhancing overall performance and efficiency.
5. ⚡High performance: Compared to current SOTA systems, ChatLearn achieves a 52% performance improvement at the 7B+7B (Policy+Reward) scale and a 137% performance improvement at the 70B+70B scale. Meanwhile, ChatLearn supports reinforcement learning training at scales exceeding 600B parameters.

Quick Start

Please refer to the documentation for a quick start.

1. Environment and Code Setup
2. End-to-End GRPO Training Pipeline for Qwen3 Model Using FSDP + vLLM
3. End-to-End GRPO Training Pipeline for Qwen3 Model Using Megatron + vLLM

Feature List

• Supports training engines such as Megatron and FSDP
• Supports inference engines including vLLM and SGLang, controlled via the runtime_args.rollout_engine parameter
• Supports reinforcement learning algorithms such as GRPO and GSPO
• Supports experiment monitoring with wandb and tensorboard
• Supports training acceleration techniques such as sequence packing, Ulysses sequence parallelism, and Group GEMM

Performance

We compared the RLHF training throughput of models with different parameter scales, adopting an N+N model configuration where both the Policy model and the Reward model have the same number of parameters. We benchmarked against DeepSpeed-Chat and OpenRLHF with 7B and 70B model configurations. For the 8 GPU setup with a 7B+7B scale, we achieved a 115% speedup; for the 32 GPU setup with a 70B+70B scale, the speedup was 208%. The larger the scale, the more pronounced the acceleration effect becomes. Additionally, ChatLearn can support even larger-scale reinforcement learning, such as at a 600B scale.

Note: The performance of DeepSpeed-Chat and OpenRLHF has already been optimized.

Roadmap

The upcoming features for ChatLearn include:

• Simplify Configuration Settings
• Support tutorials for the RL training of MoE (Mixture of Experts) models
• Support for more models
• Performance Optimization
• Support for more RL algorithms

We are continuously hiring and welcome you to contact us or submit your resume to email.