Part 7/11:
If QAR is indeed a hybrid of Q-learning and AAR, it could act as an advanced internal search and planning engine for AI models. Imagine a system that not only learns from past experiences but also systematically explores possible paths toward a goal—be it solving a math problem or devising a strategic move—while dynamically adjusting based on obstacles encountered.
This could explain the recent reports that QAR-enabled models have achieved 100% accuracy on math problems, surpassing typical benchmarks of 70-90%. Although computers are inherently good at math, the ability to internally plan and reason through complex, multi-step problems with such precision is new and exciting.