Samsung Patents a System That Teaches AI to Grade Its Own Answers

By Patentlyze Team · Updated Jul 24, 2026

Samsung is patenting a method where one AI model acts as a teacher, grading the outputs of another AI model to automatically build a dataset that makes the student model better over time.

How Samsung's AI learns from its own best and worst answers

Imagine a student who takes the same practice test ten times and then has a separate tutor circle which answers were good and which were weak. The student then studies only those paired examples, one good and one bad, to figure out what separates a strong answer from a poor one. That's essentially what Samsung's patent describes for AI models.

Here, a first AI model answers the same question multiple times, producing slightly different responses each time. A second AI model then steps in as the judge, ranking those responses by quality. The best and worst answers from each batch get paired together and saved as training data.

That paired dataset is then fed back into the system to retrain the first model, nudging it toward producing more of the preferred responses and fewer of the weak ones. The whole process is automatic, meaning Samsung could continuously improve an AI model without needing humans to hand-label every example.

How the two-model grading pipeline actually runs

The patent describes a two-stage pipeline for what researchers call preference optimization training, a technique for teaching an AI model not just what a correct answer looks like, but what a better answer looks like compared to a worse one.

• Stage 1 - Multiple inference runs: A first generative model (the one being trained) receives the same input prompt a set number of times. Because AI language models have an element of randomness in how they generate text, each run produces a slightly different response.
• Stage 2 - Automated evaluation: A second, separate generative model reviews all those responses and scores them by preference, essentially acting as an automated quality judge. This replaces the need for human annotators in the labeling step.
• Stage 3 - Pair selection: Based on the judge model's scores, the system picks pairs of responses, one that ranked high (preferred) and one that ranked low (non-preferred), from the same input prompt.
• Stage 4 - Training data generation: Those pairs become structured training examples. The first model can then be retrained on them using a preference optimization algorithm (a method that shifts a model's behavior toward outputs humans, or in this case another AI, would favor).

The key idea is closing the loop: the same family of AI models both produces and evaluates training data, reducing the human effort needed to improve the model continuously.

What this means for on-device AI quality on Galaxy phones

The expensive part of making AI models better has traditionally been paying humans to label thousands of examples of good and bad outputs. Samsung's approach, if it works reliably, cuts that cost dramatically by substituting a second AI model as the judge. This kind of automated preference optimization is increasingly common in the large-language-model space, and a patent here signals Samsung is building this capability directly into its own AI development pipeline, likely for the on-device models that power Galaxy AI features.

For you as a user, the downstream effect would be an AI assistant that gets better answers over successive software updates without Samsung needing a small army of human reviewers to sign off on every improvement. Whether Samsung's particular implementation is better than existing approaches is a separate question, but this is clearly foundational infrastructure work.

Editorial commentary on a publicly published patent application. Not legal advice.