LlamaGym

It is a framework designed to simplify the process of fine-tuning large language model (LLM)-based agents using online reinforcement learning (RL).

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It is a framework designed to simplify the process of fine-tuning large language model (LLM)-based agents using online reinforcement learning (RL). LlamaGym enables developers to train LLM agents in Gym-style environments by handling complexities such as conversation context management, episode batching, reward assignment, and Proximal Policy Optimization (PPO) setup. This allows users to focus on experimenting with agent prompting and hyperparameters without writing extensive code.

LlamaGym addresses the challenge of integrating LLM-based agents into RL environments, which traditionally require significant effort to manage. By providing an abstract Agent class, it streamlines the implementation process. Users only need to implement three abstract methods, define their base LLM, and instantiate the agent. The framework then facilitates the RL loop, enabling the agent to act, receive rewards, and terminate episodes seamlessly.

The framework is particularly useful for tasks like web data extraction, where agents can learn and adapt in real-time through reinforcement learning. It builds on the foundation of OpenAI’s Gym, which standardizes RL environments, but extends its capabilities to accommodate the unique requirements of LLM-based agents. LlamaGym is open-source and available on GitHub, offering a practical solution for researchers and developers aiming to fine-tune LLM agents efficiently.

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