slime-rl-training

npx skills add Orchestra-Research/AI-Research-SKILLs --skill slime --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# 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 ``` ┌─────────────────────────────────────────────────────────┐ │ Data Buffer │ │ - Prompt initialization and management │ │ - Custom data generation and filtering

docker pull slimerl/slime:latest
docker run --rm --gpus all --ipc=host --shm-size=16g \
Inside container
cd /root/slime && pip install -e . --no-deps
git clone https://github.com/THUDM/slime.git
cd slime
pip install -r requirements.txt
pip install -e .
Source model configuration
source scripts/models/qwen3-4B.sh