  We extend the Malitsky-Tam forward-reflected-backward (FRB) splitting method
for inclusion problems of monotone operators to nonconvex minimization
problems. By assuming the generalized concave Kurdyka-{\L}ojasiewicz (KL)
property of a quadratic regularization of the objective, we show that the FRB
method converges globally to a stationary point of the objective and enjoys
finite length property. The sharpness of our approach is guaranteed by virtue
of the exact modulus associated with the generalized concave KL property.
Numerical experiments suggest that FRB is competitive compared to the
Douglas-Rachford method and the Bo\c{t}-Csetnek inertial Tseng's method.
