Error Analysis of Discrete Flow with Generator Matching

ArXi:2509.21906v3 Announce Type: replace-cross Discrete flow models offer a powerful framework for learning distributions over discrete state spaces and have nstrated superior performance compared to the discrete diffusion models. However, their convergence properties and error analysis remain largely unexplored. In this work, we develop a unified framework grounded in stochastic calculus theory to systematically investigate the theoretical properties of discrete flow models.