  In this paper we study online caching problems where predictions of future
requests, e.g., provided by a machine learning model, are available. Typical
online optimistic policies are based on the Follow-The-Regularized-Leader
algorithm and have higher computational cost than classic ones like LFU, LRU,
as each update of the cache state requires to solve a constrained optimization
problem. In this work we analysed the behaviour of two different optimistic
policies in a \textit{batched} case, i.e., when the cache is updated less
frequently in order to amortize the update cost over time or over multiple
requests. Experimental results show that such an optimistic batched approach
outperforms classical caching policies both on stationary and real traces
