Generalized implicit reward function for generative artificial intelligence

An application server for data processing, comprising:

• one or more memories storing processor-executable code;
• and
• one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the application server to:
• obtain a generative artificial intelligence (AI) model that is a trained model comprising a set of weights and that is associated with an explicit reward model and an implicit reward model;
• zero a partition function of the implicit reward model associated with the generative AI model;
• obtain feedback data associated with the explicit reward model, the feedback data comprising preference feedback data, binary feedback data, score feedback data, or any combination thereof;
• generate the explicit reward model based at least in part on the feedback data;
• fine-tune the set of weights of the generative AI model based at least in part on a comparison of the explicit reward model and the implicit reward model and further based at least in part on the feedback data;
• receive, at the generative AI model, a query;
• and
• generate, with the generative AI model and based at least in part on the query and the fine-tuned set of weights, a response that is responsive to the query.

A non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by one or more processors to:

• obtain a generative artificial intelligence (AI) model that is a trained model comprising a set of weights and that is associated with an explicit reward model and an implicit reward model;
• zero a partition function of the implicit reward model associated with the generative AI model;
• obtain feedback data associated with the explicit reward model, the feedback data comprising preference feedback data, binary feedback data, score feedback data, or any combination thereof;
• generate the explicit reward model based at least in part on the feedback data;
• fine-tune the set of weights of the generative AI model based at least in part on a comparison of the explicit reward model and the implicit reward model and further based at least in part on the feedback data;
• receive, at the generative AI model, a query;
• and
• generate, with the generative AI model and based at least in part on the query and the fine-tuned set of weights, a response that is responsive to the query.