We establish the Clark-Kushner condition for a large class of interacting vertex-reinforced random walks on finite graphs, where the transition matrix $Q^i(x)$ of each walk depends on the joint vector $x$ of vertex occupation proportions and may have distinct rows. This allows one to study the dynamics of the vertex occupation measure by using the tools of stochastic approximation theory. However, the standard approach fails because the noise inputs are in our case not a martingale difference: they retain memory of the previous state. Using the solution of the Poisson equation for Markov chains, we decompose the noise into a martingale difference minus the increment of a bounded process -- a structure originating in Gordin's work on limit theorems for stationary processes. The key technical ingredient of our approach is a uniform geometric ergodicity bound derived from the Dobrushin contraction coefficient, which also controls the Lipschitz continuity of the solution of the Poisson equation. Our hypotheses require only that each $Q^i(x)$ be irreducible, aperiodic, and Lipschitz continuous in $x$; in particular, strictly positive entries are not assumed. Our results generalize and simplify previous arguments considered for single self-reinforced vertex-reinforced random walks.