Integrating combinatorial optimization layers into neural networks has attracted significant research interest.Existing approaches lack theoretical guarantees or fail with inexact solvers.A new theoretically-principled approach for learning with inexact combinatorial solvers is proposed.MCMC is implemented on the combinatorial space of feasible solutions to reduce computational burden in learning.