# slime-rl-training

Guides LLM post-training with RL using the slime framework, connecting Megatron-LM and SGLang. Useful for GRPO workflows and tuning GLM, Qwen3, or DeepSeek models.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 951 installs · ★ 13956
- Author: Orchestra Research
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/06-post-training/slime/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/slime-rl-training

## What it does

This skill provides setup workflows for reinforcement learning post-training using slime from Tsinghua's THUDM team. The framework pairs Megatron-LM for actor training with SGLang for high-throughput rollout generation. It supports distributed training with TP, PP, DP, and SP on GLM-4.x, Qwen3, DeepSeek V3/R1, and Llama 3.

It covers three execution workflows: synchronous GRPO via `train.py`, asynchronous rollout buffering with `train_async.py`, and multi-turn agent training using `custom_generate.py`. Running the framework requires `torch>=2.0.0`, `transformers>=4.40.0`, `ray`, and `sglang-router>=0.2.3`, or the pre-built `slimerl/slime:latest` Docker image.

If your setup demands enterprise-grade stability, consider miles instead, as slime is tailored to research setups requiring native Megatron-LM integration.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill slime-rl-training -a claude-code
```
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill slime -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/slime/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/slime/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill slime -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/slime/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill slime -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/slime/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs slime
```

2. **Manually: put the files in place**
- File: `.github/skills/slime/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill slime -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 06-post-training/slime
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/slime/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill slime -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/06-post-training/slime/SKILL.md

## When to use it
- configure GRPO training using group-relative advantage estimation
- pair SGLang rollout generation with Megatron-LM training loops
- set up asynchronous rollout buffering to minimize GPU idle time
- implement multi-turn agent training pipelines with tool calling

## Example prompts
- Help me configure a GRPO launch script for qwen3-4B using slime.
- How do I set up asynchronous training in slime with an async buffer size of 4?
- Write a custom_generate function for multi-turn tool calling in slime.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 2420c85e5a6b18b3.

BEGIN THIRD-PARTY FILE 2420c85e5a6b18b3
````text
---
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)
- **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                  │
│ - Rollout sample storage                                │
└─────────────┬───────────────────────────┬───────────────┘
              │                           │
┌─────────────▼───────────┐ ┌─────────────▼───────────────┐
│ Training (Megatron-LM)  │ │ Rollout (SGLang + Router)   │
│ - Actor model training  │ │ - Response generation       │
│ - Critic (optional)     │ │ - Reward/verifier output    │
│ - Weight sync to rollout│ │ - Multi-turn support        │
````
END THIRD-PARTY FILE 2420c85e5a6b18b3

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