We introduce RAFT2, a faster, simpler, and more accurate RAFT for optical flow. Compared with RAFT, RAFT2 is supervised with a mixture of Laplace loss. It directly regresses an initial flow for faster convergence in recurrent refinements and introduces stereo pretraining to improve generalization. RAFT2 achieves state-of-the-art on Spring benchmark with 3.69 end-point-error (EPE) and 0.36 1-pixel outlier rate (1px), representing 22.9% and 17.8% error reduction from best-published results. In addition, RAFT2 obtains the best cross-dataset generalization on KITTI(train) and Spring(train). With its high efficiency, RAFT2 operates at least 2.3x faster than mainstream methods while maintaining competitive performance, advancing the state of recurrent refinement frameworks in optical flow estimation.