slime-rl-training

name: slime-rl-training description: Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling. version: 1.0.0 author: Orchestra Research license: MIT tags: [Reinforcement Learning, Megatron-LM, SGLang, GRPO, Post-Training, GLM] dependencies: [sglang-router>=0.2.3, ray, torch>=2.0.0, transformers>=4.40.0]

slime: LLM Post-Training Framework for RL Scaling

slime is an LLM post-training framework from Tsinghua's THUDM team, powering GLM-4.5, GLM-4.6, and GLM-4.7. It connects Megatron-LM for training with SGLang for high-throughput rollout generation.

When to Use slime

Choose slime when you need:

• Megatron-LM native training with SGLang inference
• Custom data generation workflows with flexible data buffers
• Training GLM, Qwen3, DeepSeek V3, or Llama 3 models
• Research-grade framework with production backing (Z.ai)

Consider alternatives when:

• You need enterprise-grade stability features → use miles
• You want flexible backend swapping → use verl
• You need PyTorch-native abstractions → use torchforge

Key Features

• Training: Megatron-LM with full parallelism support (TP, PP, DP, SP)