中文版

slime is an LLM post-training framework for RL scaling, providing two core capabilities:

1. High-Performance Training: Supports efficient training in various modes by connecting Megatron with SGLang;
2. Flexible Data Generation: Enables arbitrary training data generation workflows through custom data generation interfaces and server-based engines.

slime is the RL-framework behind GLM-4.5 and GLM-4.6 and apart from models from Z.ai, we also supports the following models:

• Qwen3 series (Qwen3Next, Qwen3MoE, Qwen3), Qwen2.5 series;
• DeepSeek V3 series (DeepSeek V3, V3.1, DeepSeek R1);
• Llama 3.

• Our vision: slime: An SGLang-Native Post-Training Framework for RL Scaling.
• Our ideas on agentic training: Agent-Oriented Design: An Asynchronous and Decoupled Framework for Agentic RL
• v0.1.0 release note: v0.1.0: Redefining High-Performance RL Training Frameworks

• Architecture Overview
• Quick Start
• Projects Built with slime
• Arguments Walkthrough
• Developer Guide
• FAQ & Acknowledgements

Module Descriptions:

• training (Megatron): Responsible for the main training process, reads data from the Data Buffer, and synchronizes parameters to the rollout module after training.
• rollout (SGLang + router): Generates new data (including rewards/verifier outputs) and stores it in the Data Buffer.
• data buffer: A bridge module that manages prompt initialization, custom data, and rollout generation methods.

For a comprehensive quick start guide covering environment setup, data preparation, training startup, and key code analysis, please refer to:

• Quick Start Guide

We also provide examples for some use cases not covered in the quick start guide; please check examples.

slime has powered several novel research projects and production systems. Here are some notable examples:

TritonForge leverages slime's SFT & RL capabilities to train LLMs that automatically generate optimized GPU kernels. By using a two-stage training approach—supervised fine-tuning followed by reinforcement learning with multi-turn compilation feedback—TritonForge achieves remarkable results in converting PyTorch operations into high-performance Triton kernels.

APRIL introduces a system-level optimization that seamlessly integrates with slime to accelerate the rollout generation phase in RL training. By intelligently over-provisioning requests and actively managing partial completions, APRIL addresses the long-tail generation bottleneck that typically consumes over 90% of RL training time.

These projects showcase slime's versatility—from training code-generation models to optimizing RL training systems—making it a powerful foundation for both research and production deployments.

Arguments in slime are divided into three categories:

1. Megatron arguments: slime reads all arguments set in Megatron via PYTHONPATH. You can configure Megatron by passing arguments like --tensor-model-parallel-size 2.
2. SGLang arguments: All arguments for the installed SGLang are supported. These arguments must be prefixed with --sglang-. For example, --mem-fraction-static should be passed as --sglang-mem-fraction-static.
3. slime-specific arguments: Please refer to: slime/utils/arguments.py

For complete usage instructions, please refer to the Usage Documentation.

• Contributions are welcome! If you have suggestions for new features, performance tuning, or feedback on user experience, feel free to submit an Issue or PR 😊

• Use pre-commit to ensure code style consistency for your commits:

apt install pre-commit -y
pre-commit install

# run pre-commit to ensure code style consistency
pre-commit run --all-files --show-diff-on-failure --color=always

• For debugging tips, please refer to the Debugging Guide

• For frequently asked questions, please see the Q&A
• Special thanks to the following projects & communities: SGLang, Megatron‑LM, mbridge, OpenRLHF, veRL, Pai-Megatron-Patch and others.
• To quote slime, please use:

@misc{slime_github,
  author       = {Zilin Zhu and Chengxing Xie and Xin Lv and slime Contributors},
  title        = {slime: An LLM post-training framework for RL Scaling},
  year         = {2025},
  howpublished = {\url{https://github.com/THUDM/slime}},
  note         = {GitHub repository. Corresponding author: Xin Lv},
  urldate      = {2025-06-19}
}