This talk connects ideas from geometric group theory, stochastic processes, and machine learning. We present a subtle Lipschitz result showing that for a norm-preserving group action, a bound on the generating set induces a bound on distances between points within an orbit. We begin with graph symmetry as a motivating example: the action of the permutation group on vertices. We then prove our main result in the context of the general theory of stochastic orbit processes. Finally, we explore connections to equivariance in machine learning.