Interesting. Here’s my “hypothesis”. You can optimize individual prob instead of log(prob), no problem. But training has standard interpretation as maximization of joint density over dataset, that’s product of sample densities, or their sum on log domain.
You can also view this as optimization of geometric mean, while your procedure maximizes arithmetic mean of probabilities. From this, you can deduce the consequences. My guess would be overfit for “easy” samples.