skills.rest
travelinman1013
travelinman1013COMMUNITY
Explore all skills by this author →
GitHub

slime-rl-training

Integrate Megatron-LM with SGLang for RL-based post-training of large language models.

Updated Aug 23, 2026
Search Tags:#llm#reinforcement-learning#slime#rl#sglang#post-training#megatron-lm
One-click install
npx skills add https://github.com/travelinman1013/leroys-agent --skill slime-rl-training-travelinman1013
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: slime-rl-training
Source: https://github.com/travelinman1013/leroys-agent/tree/main/optional-skills/mlops/slime
Command: npx skills add https://github.com/travelinman1013/leroys-agent --skill slime-rl-training-travelinman1013

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

RL-based post-training of large language models (LLMs) requires an integrated workflow to coordinate large-scale data generation, policy optimization, and distributed training. slime provides a unified RL post-training framework that combines Megatron-LM with SGLang to enable scalable, end-to-end training loops.

Core Features & Use Cases

  • Training: Megatron-LM with full parallelism support (TP, PP, DP, SP)
  • Rollout: SGLang-based high-throughput generation with router
  • Data Management: Flexible prompt buffers and sample storage for efficient RL pipelines
  • Use Case: Develop and refine RL policies for GLM-like models with automated data generation and reward tuning.

Quick Start

Run the slime training workflow with your dataset and model configuration to start RL-based 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 run RL post-training for large language models with Megatron-LM and SGLang?▼

RL post-training for large language models is executed by integrating Megatron-LM for distributed policy optimization with SGLang for scalable rollout generation. You configure your dataset, model parameters, and reward models to run the end-to-end training loop.

What is the best way to scale rollout generation for LLM reinforcement learning?▼

Scaling rollout generation for LLM reinforcement learning is handled by SGLang, which provides high-throughput generation with a router. This works alongside Megatron-LM to coordinate data buffers and reward-driven optimization.

Can I use slime for RL post-training on GLM-family transformer architectures?▼

Yes, slime supports RL-based post-training for GLM-family models and other transformer architectures. It applies data-generation workflows, policy optimization, and distributed training to refine these models.

What parallelism strategies are supported during RL training with Megatron-LM?▼

RL training with Megatron-LM supports full parallelism including tensor, pipeline, data, and sequence parallelism. This enables distributed training at scale for large language models during the post-training phase.

How do I manage prompt data and samples for reinforcement learning pipelines?▼

Reinforcement learning pipelines manage prompt data and samples using flexible prompt buffers and sample storage. This supports efficient data generation and reward tuning within the training workflow.

Do I need a distributed compute environment for LLM reinforcement learning with slime?▼

Yes, a distributed compute environment is required for LLM reinforcement learning with slime. The framework relies on Megatron-LM and SGLang to support scalable rollout generation and distributed training across multiple nodes.

Related Skills

Explore More Software Engineering Skills→
InverterNetworkInverterNetwork

slime-rl-training

Orchestrate Megatron-LM training and SGLang rollout generation for large-scale RL post-training.

Official
Advanced
attentiondotnetattentiondotnet

grpo-rl-training

Implement GRPO/TRL reinforcement learning fine-tuning workflows for language models.

Community
Advanced
projectedanxprojectedanx
1

slime-rl-training

Train reasoning models with Megatron-LM and SGLang for RL post-training.

Community
Advanced
davpatel605-beepdavpatel605-beep

slime-rl-training

Configure scalable RL post-training for LLMs using the slime framework.

Community
Advanced