Learning has been adopted as a general behavioral model for players in repeated games. Learning offers a way that players can adopt to (possibly changing) environment. Learning guarantees high social welfare in many games (including traffic routing as well as online auctions), even when the game or the population of players is dynamically changing. The rate at which the game can change depends on the speed of convergence of the learning algorithm. If players observe all other participants, which such full information feedback classical learning algorithms offer very fast convergence. However, such full information feedback is often not available, and the convergence of classical algorithms with partial feedback is much good. In this talk we develop a black-box approach for learning where the learner observes as feedback only losses of a subset of the actions. The simplicity and black box nature of the approach allows us to use of this faster learning rate as a behavioral assumption in games. Talk based on joint work with Thodoris Lykouris and Karthik Sridharan.