LlamaGym

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SKU: llamagym
LlamaGym is an open-source Python framework designed to simplify the fine-tuning of large language model (LLM) agents through online reinforcement learning. By providing a standardized environment similar to OpenAI's Gym, LlamaGym allows developers to efficiently train LLM-based agents by managing conversation context, episode batching, reward assignment, and proximal policy optimization (PPO) setup. This framework enables rapid experimentation with agent prompting and hyperparameters across various Gym environments, facilitating the development of more capable and responsive AI agents.
AI framework reinforcement learning LLM fine-tuning open-source Python
Developing AI agents that learn and adapt through online reinforcement learning.
Fine-tuning large language models for specific tasks within standardized environments.
Experimenting with agent prompting and hyperparameters to optimize performance.
Integrating LLM-based agents into Gym-style reinforcement learning workflows.
LlamaGym provides substantial automation for reinforcement learning workflows by handling conversation context management, episode batching, reward assignment, and PPO implementation. However, it requires explicit human guidance for prompt engineering, environment configuration, and hyperparameter selection. The framework automates repetitive RL mechanics but leaves strategic decisions like reward function design and environment selection to developers.
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