wehub-resource-sync
79 lines
3.7 KiB
Markdown
79 lines
3.7 KiB
Markdown
# MCP-RL: Train AI Agents to Master MCP Servers with Reinforcement Learning
|
|
|
|
In this tutorial, we train an LLM agent to become an expert at using an MCP (Model Context Protocol) server through reinforcement learning. Instead of just connecting a model to tools and hoping it figures things out, we use [OpenPipe's ART framework](https://github.com/OpenPipe/ART) to let the model practice using those tools thousands of times. This way it learns which strategies work and which don't.
|
|
|
|
A small 3B model learns to explore database schemas, write correct SQL JOINs, and answer multi-step questions. Skills it never had out of the box.
|
|
|
|
## What We're Building
|
|
|
|
- A custom MCP server (FastMCP + SQLite) with a company database containing departments, employees, and projects
|
|
- A full MCP-RL training pipeline using ART (Agent Reinforcement Trainer) and GRPO
|
|
- RULER for automatic reward scoring — no hand-labeled data needed
|
|
- A trained agent that reliably chains multiple tools to answer complex database questions
|
|
|
|
## Tech Stack
|
|
|
|
- [OpenPipe ART](https://github.com/OpenPipe/ART): RL framework for training LLM agents with GRPO
|
|
- [RULER](https://art.openpipe.ai/features/ruler): LLM-as-judge for automatic reward scoring
|
|
- Qwen 2.5 3B Instruct base model for RL fine-tuning
|
|
|
|
|
|
## The MCP Server
|
|
|
|
`mcp_server.py` spins up a local MCP server with 3 tables and 3 tools:
|
|
|
|
| Tool | Description |
|
|
|------|-------------|
|
|
| `list_tables()` | Discover available tables |
|
|
| `describe_table(table_name)` | Get schema and column info |
|
|
| `run_query(sql)` | Execute read-only SELECT queries |
|
|
|
|
The database contains interconnected company data (departments, employees, projects) that forces the agent to learn multi-step reasoning, exploring schemas before writing queries, using JOINs across tables, and handling errors gracefully.
|
|
|
|
## Getting Started
|
|
|
|
### Prerequisites
|
|
|
|
- Google Colab with T4 GPU (or any environment with 16GB+ VRAM)
|
|
- OpenRouter API key (for scenario generation and RULER evaluation)
|
|
|
|
### Setup
|
|
|
|
1. **Set your API key** in the notebook:
|
|
```python
|
|
import os
|
|
os.environ["OPENROUTER_API_KEY"] = "your-key-here"
|
|
```
|
|
|
|
2. **Run the notebook**: It handles all installation, server startup, and training.
|
|
|
|
|
|
## How It Works
|
|
|
|
1. MCP Server runs locally with a SQLite database
|
|
2. ART generates training scenarios that are diverse database questions of varying complexity
|
|
3. The agent attempts each scenario multiple times (rollouts), interacting with the MCP server
|
|
4. RULER scores each attempt by comparing trajectories without requirinf any labeled data
|
|
5. GRPO updates the model weights reinforcing good strategies, suppressing bad ones
|
|
6. At each repetition the agent gets progressively better at tool use
|
|
|
|
## Tips for better results
|
|
|
|
- Enrich tool descriptions with actual schema info before generating scenarios, otherwise the generator LLM has a risk of hallucinating fake table names
|
|
- T4 GPU note: Use `float16` instead of `bf16`, and keep `max_seq_length` at 4096-8192
|
|
- If the model hallucinates errors, strengthen the system prompt to explicitly say the database is working and tools should always be used
|
|
- Increase rollouts (6-8) if RULER scores show no variance. GRPO needs differentiated scores to learn
|
|
|
|
---
|
|
|
|
## 📬 Stay Updated with Our Newsletter!
|
|
**Get a FREE Data Science eBook** 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. [Subscribe now!](https://join.dailydoseofds.com)
|
|
|
|
[](https://join.dailydoseofds.com)
|
|
|
|
---
|
|
|
|
## Contribution
|
|
|
|
Contributions are welcome! Please fork the repository and submit a pull request with your improvements.
|