Slime Rl Training

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)
• Rollout: SGLang-based high-throughput generation with router
• Data Buffer: Flexible prompt management and sample storage
• Models: GLM-4.x, Qwen3, DeepSeek V3/R1, Llama 3

Architecture Overview