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slime-rl-training

Train large language models with GRPO, GSPo, and PPO reinforcement learning.

2|1|Updated May 10, 2026
Search Tags:#training#reinforcement learning#scaling#rl#language models#large models
One-click install
npx skills add https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture --skill slime-rl-training-zli5460
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: slime-rl-training
Source: https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture/tree/main/optional-skills/mlops/slime
Command: npx skills add https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture --skill slime-rl-training-zli5460

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires ray, torch, transformers, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides comprehensive guidance and tools for training large language models with reinforcement learning, enabling efficient customization and scaling of model training workflows.

Core Features & Use Cases

  • Guided Workflow Setup: Step-by-step instructions for data preparation, model configuration, and training execution.
  • Supports Multiple Algorithms: Implements training with GRPO, GSPo, PPO, and reinforcement learning techniques.
  • Use Case: Ideal for researchers and engineers developing RL-powered AI assistants or customizing models for specific tasks using custom datasets and reward functions.

Quick Start

Use the slime-rl-training skill to set up a reinforcement learning training process by configuring model scripts, preparing data, and executing the training command.

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 reinforcement learning training for large language models?▼

To set up reinforcement learning training for large language models, you need to configure model scripts, prepare your datasets, and execute the training command using frameworks like Torch and Transformers.

What reinforcement learning algorithms can I use for training language models?▼

You can use multiple reinforcement learning algorithms for training language models, including GRPO, GSPo, and PPO, which are supported to help develop advanced AI agents.

How do I customize reward functions for RL model training?▼

Custom reward design for RL model training is supported directly through the workflow, allowing you to define specific reward functions tailored to your custom datasets and targeted tasks.

Can I use Ray for scaling reinforcement learning workflows in production?▼

Yes, Ray is supported as a dependency for scaling reinforcement learning workflows, enabling adaptable and efficient scaling of model training across both research and production environments.

Does this tool support multi-task evaluation during large model training?▼

Multi-task evaluation is supported during large model training, allowing you to assess model performance across different objectives while managing data flexibly throughout the process.

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