This paper proposes a new framework of algorithms that is extended from the celebrated extragradient algorithm. Min-max problem has attracted increasing attention because of application in machine learning missions such as generative adversarial network (GAN) training. While there has been exhaustive researches on convex-concave setting, problem on nonconvex-nonconcave setting faces many challenges, such as convergence to limit cycles. Since general min-max optimization is proved intractable, recent research focus has been put on structured problems. One of these follows the weak Minty variational inequality (weak MVI), which is motivated by relaxing Minty variational inequality without compromising convergence guarantee of extragradient algorithm. Existing extragradient-type algorithms involve one exploration step and one update step per iteration. We analyze the algorithms with multiple exploration steps and show that current assumption can be further relaxed when more exploration is introduced. Furthermore, we design an adaptive algorithm that explores until the optimal improvement is achieved. This process exploit information from the whole trajectory and effectively tackle cyclic behaviors.