The algorithms I know for solving 3SAT typically have exponential run-time distributions which become wider in their PDF as the number of variables, $N$, increases. For the exponential distribution this specifically means that the relative variance, $var_{rel} = \frac{var(PDF)}{mean^2(PDF)}$, of the PDF with increasing $N$ is constant (exactly $var_{rel}(N)=1$ actually) and the kurtosis is constant as well.
Are there any algorithms whose relative variance in the PDF of the run-time distribution would be expected to decrease, specifically as $\sim \frac{1}{N}$, and/or where the kurtosis decreases?
This would seem intuitively strange to me since that would mean that the algorithm would become more "deterministic" (in the physics sense), but I want to make sure.
Is there a straightforward interpretation of what it would mean for a non-trivial, competitive algorithm to exhibit such a trait of $var_{rel}\sim \frac{1}{N}$ and decreasing kurtosis?