“Through this adaptive learning process, the model refines its predictions to enhance quality,” the company explained.
And finally in the continuous improvement phase, enterprise users create data, which are essentially different LLM inputs, by interacting with the model, which can be used to optimize model performance further.
TAO can increase the efficiency of inexpensive models
Databricks said it used TAO to not only achieve better model quality than fine-tuning but also upgrade the functionality of inexpensive open-source models, such as Llama, to meet the quality of more expensive proprietary models like GPT-4o and o3-mini.
“Using no labels, TAO improves the performance of Llama 3.3 70B by 2.4% on a broad enterprise benchmark,” the team wrote.
