This work proposes a novel variation for VI, based on a combination of MCMC/SMC and stochastic gradients. The key idea is using a conditional Markov transition kernel to obtain increasingly refined estimates of the KL gradients. The empirical results are provided on smaller datasets and it has been pointed out that the paper would improve, if scalability of the method could have been illustrated via experiments on larger datasets.