LlamaGym

Fine-tune LLM agents with online reinforcement learning

open-sourceagent-frameworks

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Overview

LlamaGym is a Python framework that simplifies fine-tuning LLM-based agents using online reinforcement learning. It addresses the gap between traditional RL environments and modern LLM agents, which typically don't learn continuously through interaction. The framework provides a single Agent abstract class that handles complex boilerplate code including LLM conversation context management, episode batching, reward assignment, and PPO setup. Built to work with OpenAI Gym-style environments, LlamaGym enables researchers and developers to quickly iterate on agent prompting and hyperparameters without getting bogged down in implementation details. The framework integrates with popular ML libraries like Transformers, supporting models such as Llama-2-7b. Users only need to implement three simple abstract methods: defining the system prompt, formatting observations for the LLM, and extracting actions from model responses. This streamlined approach makes it significantly easier to experiment with reinforcement learning for language model agents, opening up possibilities for training more adaptive and continuously learning AI systems.

Pros

+ Drastically reduces boilerplate code needed to integrate LLMs with RL environments, handling complex aspects like conversation context and reward assignment automatically
+ Simple API requiring only 3 abstract method implementations makes it accessible to both RL researchers and LLM practitioners
+ Compatible with standard Gym environments and popular ML frameworks like Transformers, enabling easy integration into existing workflows

Cons

- Relatively small community and ecosystem compared to more established RL or LLM frameworks
- Limited to Gym-style environments, which may not cover all potential use cases for RL-based LLM training
- Requires solid understanding of both reinforcement learning concepts and LLM fine-tuning, creating a steep learning curve for newcomers

Use Cases

• Training LLM agents to play games like Blackjack, where the agent learns optimal strategies through trial and error
• Fine-tuning language models for sequential decision-making tasks in business or research contexts
• Academic research combining reinforcement learning with large language models to study emergent behaviors and learning patterns

Getting Started

1. Install LlamaGym via pip: `pip install llamagym` 2. Implement the Agent class with three abstract methods: get_system_prompt(), format_observation(), and extract_action() 3. Set up your base LLM model and tokenizer, then create your RL training loop calling agent.act(), agent.assign_reward(), and agent.terminate()