Skill · em Dados, IA e pesquisa

slime-rl-training

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.

Procedência

Antes de instalar

3 arquivos · 30 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/slime-rl-training/.

d=".claude/skills/slime-rl-training"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/post-training-slime"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api-reference.md" "$u/references/api-reference.md" \
  -o "$d/references/troubleshooting.md" "$u/references/troubleshooting.md"

Global: instala em ~/.claude/skills/slime-rl-training/.

d="$HOME/.claude/skills/slime-rl-training"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/post-training-slime"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api-reference.md" "$u/references/api-reference.md" \
  -o "$d/references/troubleshooting.md" "$u/references/troubleshooting.md"

Codex

Neste projeto: instala em .agents/skills/slime-rl-training/.

d=".agents/skills/slime-rl-training"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/post-training-slime"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api-reference.md" "$u/references/api-reference.md" \
  -o "$d/references/troubleshooting.md" "$u/references/troubleshooting.md"

Global: instala em ~/.agents/skills/slime-rl-training/.

d="$HOME/.agents/skills/slime-rl-training"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/post-training-slime"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api-reference.md" "$u/references/api-reference.md" \
  -o "$d/references/troubleshooting.md" "$u/references/troubleshooting.md"

Antigravity

Neste projeto: instala em .agents/skills/slime-rl-training/.

d=".agents/skills/slime-rl-training"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/post-training-slime"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api-reference.md" "$u/references/api-reference.md" \
  -o "$d/references/troubleshooting.md" "$u/references/troubleshooting.md"

Global: instala em ~/.gemini/antigravity-cli/skills/slime-rl-training/.

d="$HOME/.gemini/antigravity-cli/skills/slime-rl-training"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/post-training-slime"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api-reference.md" "$u/references/api-reference.md" \
  -o "$d/references/troubleshooting.md" "$u/references/troubleshooting.md"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.slime-rl-training

Prévia do SKILL.md

---
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                  │
…

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