Nvidia patents method to improve AI accuracy by correcting training data
Nvidia describes a technique for training artificial intelligence models by creating a copy of the network to generate new training data. The system adjusts the probability of certain outcomes in this data to help the model learn specific skills without forgetting general knowledge.
In the filing’s own words
Apparatuses, systems, and techniques to perform training and finetuning to increase accuracy of neural networks. In at least one embodiment, a processor performs a neural network training by generating a clone of the neural network to generate training data through minifinetuning which may then be used to train the neural network, facilitating an increase in domain-specific knowledge while also preventing degeneralization.
What exactly they claim (claim 1)
1. One or more processors comprising: circuitry to: clone a first neural network to generate a second neural network; generate a distribution corresponding to one or more probabilities of one or more results generated by the first neural network in response to an input; generate a modified distribution based, at least in part, on a distribution correction function that adjusts a probability of the one or more results of the distribution; and update one or more parameters of the second neural network based, at least in part, on the modified distribution.
Facts
- Company
- Nvidia (NVIDIA Corporation)
- Official title
- NEURAL NETWORK TRAINING USING CORRECTIVE DISTILLATION
- Publication number
- US20260278346A1
- Application number
- 19568694
- Filed
- Mar 16, 2026
- Published
- Sep 17, 2026
- Inventors
- 4
- Claims
- 20
Classified under
G06N 3/047 Physics & computingG06N 3/045 Physics & computingMore from Nvidia in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.