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slime-rl-training

Train LLMs with reinforcement learning using slime and Megatron-LM.

3|1|Updated May 19, 2026
Search Tags:#LLM#GLM#RL#SGLang#Megatron-LM
One-click install
npx skills add https://github.com/Quill-Agent/Quill-Agent --skill slime-rl-training-quill-agent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: slime-rl-training
Source: https://github.com/Quill-Agent/Quill-Agent/tree/main/optional-skills/mlops/slime
Command: npx skills add https://github.com/Quill-Agent/Quill-Agent --skill slime-rl-training-quill-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sglang-router>=0.2.3, ray, torch>=2.0.0, transformers>=4.40.0, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of optimizing large language models (LLMs) post-training using reinforcement learning (RL) with the slime framework, enabling scalable improvements and advanced features.

Core Features & Use Cases

  • LLM Post-Training: Supports post-training optimization for LLMs with reinforcement learning.
  • Custom Data Generation: Offers flexible data generation workflows for RL training.
  • Megatron-LM Integration: Seamless integration with Megatron-LM for training.
  • Use Case: Ideal for researchers and developers who need to scale RL training for LLMs and require tight integration with Megatron-LM.

Quick Start

Use the slime-rl-training skill to train a GLM model with RL using the provided data and model configurations.

Frequently Asked Questions about slime-rl-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize LLM post-training with reinforcement learning using Megatron-LM?▼

You can optimize LLM post-training with reinforcement learning by using the slime framework, which provides scalable model improvements and seamless integration with Megatron-LM for distributed training.

What's the best way to set up custom data generation for RL training of GLM models?▼

The best way to set up custom data generation for RL training is through the slime framework's flexible data generation workflows, which allow you to configure tailored prompts and datasets for your GLM model.

Do I need specific Python packages to run RL post-training with slime?▼

Yes, running RL post-training with slime requires specific Python packages including sglang-router, ray, torch, and transformers to support the distributed model training and inference infrastructure.

Can I use slime for scalable reinforcement learning training across large language models?▼

Yes, you can use slime for scalable reinforcement learning training across large language models, as the framework is specifically designed to support scalable training and advanced RL features.

Does slime support integration with SGLang for LLM post-training?▼

Yes, slime supports integration with SGLang for LLM post-training through the required sglang-router dependency, enabling efficient routing and inference during the reinforcement learning workflow.

What are the limitations of using slime for RL training with Megatron-LM?▼

The main limitations of using slime for RL training involve managing the complex dependencies required for Megatron-LM integration, and ensuring your environment meets the advanced configuration needs for scalable distributed training.

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