LlamaGym

LlamaGym is an open-source Python framework designed to simplify the fine-tuning of large language model (LLM) agents using online reinforcement learning (RL) in Gym-style environments. It provides a single abstract Agent class that handles complexities such as LLM conversation context management, reward assignment, episode batching, and PPO (Proximal Policy Optimization) training setup. This allows developers to quickly experiment with agent prompting and hyperparameters for various RL tasks, making it ideal for research and development involving interactive AI agents.

Key Features:

• Single abstract Agent class that manages RL implementation details and conversation context.
• Simplified integration with Gym-style RL environments for easy experimentation.
• Supports reward assignment, episode batching, and PPO-based fine-tuning.
• Flexible hyperparameter experimentation to optimize agent learning.

Use Cases:

• Training AI agents for game strategy learning (e.g., Blackjack).
• Robotic control simulations and interactive decision-making tasks.
• Research and experimentation in online reinforcement learning with LLMs.

Technical Specifications:

• Python-based library compatible with major LLM architectures.
• Requires Gym environment compatibility for reinforcement learning tasks.
• Minimal dependencies designed for computational efficiency and ease of use.