skills.rest
zulumonkeymetallic
zulumonkeymetallicCOMMUNITY
Explore all skills by this author →
GitHub

slime-rl-training

Guide RL post-training of GLM models using slime with Megatron-LM and SGLang.

Updated Aug 27, 2026
Search Tags:#glm#slime#rl#sglang#post-training#megatron-lm
One-click install
npx skills add https://github.com/zulumonkeymetallic/bob --skill slime-rl-training-zulumonkeymetallic
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: slime-rl-training
Source: https://github.com/zulumonkeymetallic/bob/tree/main/optional-skills/mlops/slime
Command: npx skills add https://github.com/zulumonkeymetallic/bob --skill slime-rl-training-zulumonkeymetallic

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sglang-router>=0.2.3, ray, torch>=2.0.0, transformers>=4.40.0, and includes references (resource) components.

What problem does it solve?

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.

Core Features & Use Cases

  • Guides on setting up RL post-training pipelines for LLMs using slime
  • Covers Megatron-LM integration and SGLang-driven rollout
  • Addresses data generation, buffering, and multi-turn RL scenarios

Quick Start

Follow the slime-rl-training guidance to configure an RL post-training workflow with slime.

Frequently Asked Questions about slime-rl-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up RL post-training for GLM models using slime and Megatron-LM?▼

slime provides a Megatron+SGLang framework for RL post-training of LLMs. You configure the pipeline using the slime-rl-training guidance to manage Megatron-LM integration, SGLang rollout, and data buffering for GLM models.

What is the slime framework used for in LLM post-training?▼

The slime framework provides a Megatron+SGLang architecture for scaling RL post-training of LLMs. It manages SGLang-driven rollout, data generation, and buffering workflows for GLM models.

Can I use slime for multi-turn RL scenarios and custom data generation?▼

Yes, slime explicitly addresses data generation, buffering, and multi-turn RL scenarios. The framework provides the necessary workflows to scale these reinforcement learning processes for GLM models.

Do I need SGLang and Ray installed to perform slime RL post-training?▼

Yes, slime requires sglang-router, Ray, torch, and transformers as dependencies. These libraries provide the necessary infrastructure for SGLang rollout and Megatron-LM integration during RL post-training.

How does SGLang rollout work with Megatron-LM in slime?▼

SGLang manages rollout generation while Megatron-LM handles training scaling within the slime framework. This integration enables efficient data generation and buffering workflows for RL post-training of GLM models.

What are the limitations of using slime for RL post-training?▼

slime is optimized for GLM models and requires tight Megatron-LM integration with SGLang. If your LLM architecture lacks GLM compatibility or your environment cannot support Ray and torch dependencies, slime may not suit your RL scaling needs.

Related Skills

Explore More Software Engineering Skills→
richardnguyen0715richardnguyen0715

slime-rl-training

Train LLMs with reinforcement learning using Megatron-LM and SGLang.

Community
Advanced
SethyPagnaSethyPagna

slime-rl-training

Automate reinforcement-learning post-training setup for large language models.

Community
Advanced
ovachieverovachiever
52

grpo-rl-training

Apply GRPO/RL training with TRL to optimize language models.

Community
Advanced
ovachieverovachiever
52

training-llms-megatron

Train large language models with Megatron-Core using configurable 3D and 4D parallelism.

Community
Advanced