Slime RL Training
Skill Verified ActiveProvides 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.
To enable users to perform advanced LLM post-training with Reinforcement Learning using a specific, integrated framework (slime), facilitating custom data generation and scaling training efforts.
Features
- LLM post-training with RL using slime framework
- Integration of Megatron-LM and SGLang
- Support for various LLM families (GLM, Qwen, DeepSeek, Llama 3)
- Multiple training workflows (standard, async, multi-turn)
- Detailed configuration, installation, and troubleshooting guides
Use Cases
- Training GLM models with RL
- Implementing custom data generation workflows for LLM training
- Achieving tight Megatron-LM integration for RL scaling
- Fine-tuning large language models with reinforcement learning techniques
Non-Goals
- Enterprise-grade stability features (suggests 'miles')
- Flexible backend swapping (suggests 'verl')
- PyTorch-native abstractions (suggests 'torchforge')
- General LLM pre-training or inference outside of RL post-training
Workflow
- Prepare Data
- Configure Model
- Launch Training (Standard/Async/Multi-Turn)
- Monitor Training
Practices
- Reinforcement Learning
- Model Training
- LLM Fine-tuning
- Distributed Systems
Prerequisites
- Docker environment or Megatron-LM + SGLang installed
- Model checkpoint (HuggingFace or Megatron format)
- Training data in JSONL format
Trust
- info:Issues AttentionWith 17 open and 4 closed issues in the last 90 days, the closure rate is 19%, indicating slow maintainer response for ongoing issues.
Installation
npx skills add davila7/claude-code-templatesRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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