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LlamaGym

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What is LlamaGym?

LlamaGym is an open-source framework designed to simplify fine-tuning LLM-based agents using reinforcement learning in Gym environments. It provides an abstract Agent class that handles conversation context, reward assignment, and PPO setup, enabling quick experimentation with agent prompts and hyperparameters. The tool is ideal for researchers and developers looking to integrate LLMs into RL workflows without extensive coding. While still a work in progress, LlamaGym emphasizes simplicity and ease of use.

Features

Abstract Agent class simplifies RL integration
Handles conversation context and reward assignment
Supports PPO setup for fine-tuning LLMs
Works with any Gym-style environment
Enables quick iteration on agent prompts

Pros and Cons of LlamaGym

Pros

Reduces coding complexity for RL integration
Supports flexible agent prompting strategies
Facilitates experimentation with hyperparameters
Compatible with various Gym environments
Encourages community contributions and improvements

Cons

Online RL convergence can be challenging
Limited compute efficiency compared to alternatives
Requires hyperparameter tuning for optimal performance

LlamaGym Use Cases

Fine-tuning LLMs for game-playing agents
Training agents in simulated environments
Experimenting with RL-based prompt optimization
Developing conversational agents with RL feedback

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