Quantum reservoir computing (QRC) is a machine learning paradigm in which a quantum system is used to perform information processing. A prospective approach to its physical realization is a photonic platform in which continuous variable quantum information methods are applied. The simplest continuous variable quantum states are Gaussian states, which can be efficiently simulated classically. As such, they provide a benchmark for the level of performance that non-Gaussian states should surpass in order to give a quantum advantage. In this article we propose two methods to increase the information processing capacity of QRC with Gaussian states compared to previous QRC schemes. We consider better utilization of the measurement distribution by sampling its cumulative distribution function. We show it provides memory in areas that conventional approaches are lacking, as well as improving the overall processing capacity of the reservoir. We also consider storing past measurement results in classical memory, and show that it improves the memory capacity and can be used to mitigate the effects of statistical noise due to finite measurement ensemble.