Since the seminal work of Lai and Robbins (1985) we know bandit strategies with normalized regret of order (i) 1/sqrt(T) for any stochastic bandit, and (ii) log(T) / T for 'benign' distributions. In Bubeck and Slivkins (2012) we designed a new strategy which extends property (i) to adversarial bandits while still having the fast rate given in (ii). I will present this algorithm and I will also discuss the possibility of even faster rates of order 1/T when extra information is available.