Comment by wongarsu

> Curiously, fusing a model with itself also boosted performance

Back in the GPT2 to GPT3 era this was a pretty common thing to do. You are effectively taking more samples from the space of likely outputs. If your model can do the task 60% of the time just take 5-10 samples and implement some kind of majority voting

It became less common to use as models got high accuracy on problems where combining results is trivial. But with a more complex judge (a competent LLM) you can still get better results by just sampling more of the output space and picking out the best aspects