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slime-rl-training

Orchestrate RL post-training workflows for LLMs with Megatron and SGLang.

Updated Apr 21, 2026
Search Tags:#tool-calling#distributed-training#slime#rl-training#sglang#reward-modeling#megatron-lm
One-click install
npx skills add https://github.com/samuelmukoti/myai-agent --skill slime-rl-training-samuelmukoti
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: slime-rl-training
Source: https://github.com/samuelmukoti/myai-agent/tree/main/optional-skills/mlops/slime
Command: npx skills add https://github.com/samuelmukoti/myai-agent --skill slime-rl-training-samuelmukoti

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

slime provides a guided, production-ready workflow for post-training reinforcement learning on large language models using Megatron-LM and SGLang, unifying data generation, rollout, and evaluation in a scalable pipeline.

Core Features & Use Cases

  • RL-centric post-training orchestration for GLM, Qwen, and Llama models using slime
  • Data-buffered rollout generation, reward modeling, and multi-turn training support
  • Researchers and engineers deploying RL-based fine-tuning with Megatron-LM and SGLang in academic or industry settings

Quick Start

Install slime, configure the model scripts under scripts/models, and run the training workflow to start RL post-training.

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 large language models using Megatron-LM and SGLang?▼

RL post-training for large language models using Megatron-LM and SGLang requires installing the slime framework, configuring model scripts under the scripts/models directory, and executing the training workflow to orchestrate data generation, rollout, and reward modeling.

Can I use slime for multi-turn RL fine-tuning on Qwen and Llama models?▼

Yes, slime supports multi-turn RL fine-tuning and data-buffered rollout generation for GLM, Qwen, and Llama-family models, providing a unified pipeline for both academic research and production industry deployments.

What is the best way to configure reward modeling workflows for distributed training?▼

The best way to configure reward modeling workflows for distributed training is using slime's modular configuration scheme, which integrates Megatron, SGLang, and slime arguments to scale post-training data generation and evaluation.

Does slime support tool-calling and multi-turn interactions during RL training?▼

Yes, slime supports tool-calling and multi-turn training interactions during RL post-training, allowing researchers and engineers to orchestrate complex rollout generation and reward modeling workflows for large language models.

Why do I need a reward model for post-training large language models?▼

A reward model is needed for post-training large language models to evaluate and guide the reinforcement learning process, enabling the slime framework to scale data-buffered rollouts and optimize model performance across GLM, Qwen, or Llama architectures.

What troubleshooting resources are available if my slime RL training workflow fails?▼

The slime framework provides documented model scripts and troubleshooting resources within its optional components references and assets, helping resolve issues during distributed RL post-training with Megatron-LM and SGLang.

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