LlamaGym: Simplifying Fine-Tuning of LLM Agents with Reinforcement Learning

Overview

LlamaGym is an innovative open-source tool designed to streamline the process of fine-tuning large language model (LLM) agents using online reinforcement learning (RL). Built on the foundations of OpenAI's Gym, LlamaGym addresses the complexities associated with integrating LLMs into RL environments.

How to Use

Getting started with LlamaGym is straightforward. After installing the package using the command:

pip install llamagym

You will need to implement three abstract methods on the Agent class to customize your agent's behavior. For instance:

from llamagym import Agent

class BlackjackAgent(Agent):
    def get_system_prompt(self) -> str:
        return "You are an expert blackjack player."

    def format_observation(self, observation) -> str:
        return f"Your current total is {observation[0]}"

    def extract_action(self, response: str):
        return 0 if "stay" in response else 1

Purposes

LlamaGym is designed for developers and researchers looking to enhance LLM capabilities in real-time scenarios, such as gaming or data extraction, by leveraging reinforcement learning techniques.

Benefits for Users

• Simplification: Streamlines the process of fine-tuning LLM agents.
• Flexibility: Allows experimentation with various prompting and hyperparameters.
• Integration: Easily integrates with existing Gym environments for seamless RL applications.

Alternatives

While LlamaGym stands out for its focus on LLMs, alternatives like OpenAI Gym and Stable Baselines offer broader RL capabilities without specific