Despite the significant interest and progress in reinforcement learning (RL) problems with adversarial corruption, current works are either confined to the linear setting or lead to an undesired 𝒪̃(√(T)ζ) regret bound, where T is the number of rounds and ζ is the total amount of corruption. In this paper, we consider contextual bandits with general function approximation and propose a computationally efficient algorithm to achieve a regret of 𝒪̃(√(T)+ζ). The proposed algorithm relies on the re…