slime-rl-training

Reinforcement learning post-training for large language models is set up by unifying Megatron-LM training with SGLang rollout. This framework provides configurable data buffers and pipelines to efficiently optimize model behavior using algorithms like GRPO and PPO.

Yes, Megatron-LM and SGLang are tightly integrated to support RL training for GLM, Qwen, and Llama family models. This combination enables efficient rollout generation and scalable training workflows on Linux or macOS environments.

Scaling on-policy and off-policy RL training workflows is achieved by leveraging a unified framework that combines Megatron-LM training and SGLang rollout. It supports multi-turn and tool-using scenarios with flexible data generation and configurable buffers.

To start RL-based post-training with GRPO or PPO algorithms, provide your model configuration and training data to initialize the pipeline. The framework manages data buffering, rollout generation, and configurable training and evaluation loops automatically.

Yes, slime RL training supports multi-turn, tool-using, on-policy, and off-policy RL training scenarios. It handles these complex interactions through flexible data generation workflows and configurable buffers integrated with Megatron-LM and SGLang.

Using slime for LLM post-training requires tight integration with Megatron-LM, SGLang, and RL algorithms, targeting ML engineers and researchers. It is specifically designed for GLM-family models on Linux or macOS, which may limit compatibility with other architectures or operating systems.