# Lock-In Law

Majority-vote test-time reinforcement learning makes each question lock onto one answer during training, often a wrong one on hard questions, and until now which answer that is became known only after training. The work treats this as a short race driven by sampling noise between the answers that are close at the start, and gives a closed-form probability for how the network kernel shifts the gap of the two leaders through updates of the trailing answers. In LoRA training on MMLU-Pro multiple-choice questions with three models from two families, predictions from a frozen-kernel chain computed before training forecast which answer each question locks into better than a logistic model refitted on the outcomes, using no fitted constants.

Majority-vote test-time reinforcement learning makes each question lock onto one answer during training, often a wrong one on hard questions, and until now which answer that is became known only after training. The work treats this as a short race driven by sampling noise between the answers that are close at the start, and gives a closed-form probability for how the network kernel shifts the gap of the two leaders through updates of the trailing answers. In LoRA training on MMLU-Pro multiple-choice questions with three models from two families, predictions from a frozen-kernel chain computed before training forecast which answer each question locks into better than a logistic model refitted on the outcomes, using no fitted constants.