Salesforce Research has unveiled AgentOhana, a complete agent data collection and training pipeline designed for large language models. This innovative system offers a new method for training artificial intelligence (AI) agents, aiming to improve their understanding and generation of human language. AgentOhana collects data from a variety of sources, including text-based games, to create a diverse dataset for training AI models.
The system utilises reinforcement learning from human feedback (RLHF), an approach that enables the AI to learn from its mistakes and improve over time. It also employs a new technique called Proximal Policy Optimisation, which helps the AI to make better decisions by balancing exploration and exploitation.
AgentOhana has shown impressive results in early tests, with AI agents demonstrating improved performance in text-based games and better comprehension of complex language tasks. This development could have significant implications for the future of AI, potentially enhancing the capabilities of chatbots, virtual assistants, and other AI-powered tools.
Salesforce Research’s innovation is a step forward in the quest for more sophisticated AI models, paving the way for AI agents that can understand and respond to human language more effectively. The introduction of AgentOhana signals a promising future for AI development, with potential benefits for businesses and consumers alike.
Go to source article: https://www.marktechpost.com/2024/03/02/salesforce-research-introduces-agentohana-a-comprehensive-agent-data-collection-and-training-pipeline-for-large-language-model/