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train-rl

Create reinforcement learning training scripts for language models using the ART framework.

10.6k|971|Updated Mar 10, 2025
Search Tags:#llm#fine-tuning#agent training#reinforcement learning#rl#grpo
One-click install
npx skills add https://github.com/OpenPipe/ART --skill train-rl
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: train-rl
Source: https://github.com/OpenPipe/ART/tree/main/.agents/skills/train-rl
Command: npx skills add https://github.com/OpenPipe/ART --skill train-rl

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openai, art.

What problem does it solve?

This Skill helps users create and train reinforcement learning (RL) agents using the ART framework, simplifying the process of fine-tuning language models with experience.

Core Features & Use Cases

  • Interactive Wizard: Guides users through setting up RL training by asking targeted questions.
  • Flexible Training: Supports both single-turn and multi-turn agent interactions.
  • Reward Methods: Accommodates programmatic rewards and LLM-as-judge (RULER) scoring.
  • Use Case: Train an agent to play a text-based adventure game by defining the game's rules, actions, and a reward function, then generating a training script.

Quick Start

Use the train-rl skill to start an interactive session to create a reinforcement learning training script for a new agent.

Frequently Asked Questions about train-rl

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

FAQPage Schema
How do I train a reinforcement learning agent for an LLM using GRPO?▼

To train a reinforcement learning agent using GRPO, you can use an interactive wizard to define agent tasks, configure reward mechanisms, and tune hyperparameters to generate runnable Python training scripts.

Can I use LLM-as-judge for reward functions in RL fine-tuning?▼

Yes, you can use LLM-as-judge for reward functions in RL fine-tuning by configuring RULER scoring mechanisms alongside programmatic rewards during the training script generation process.

Does the ART framework support multi-turn tool-calling agents?▼

Yes, the ART framework supports multi-turn scenarios including those involving tool-calling, allowing you to train agents for complex interactive workflows beyond single-turn tasks.

What do I need to set up before fine-tuning a language model with reinforcement learning?▼

Before fine-tuning a language model with reinforcement learning, you need the OpenAI and ART dependencies installed, and a defined agent task with corresponding reward mechanisms ready for configuration.

What is the best way to define a reward mechanism for a text-based game agent?▼

The best way to define a reward mechanism for a text-based game agent is by establishing game rules and actions, then configuring either programmatic rewards or RULER scoring within the training script.

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