Recently a strategy was proposed that offered modest performance improvement over REINVENT called 'best agent reminder' (BAR) , although this was implemented on a graph-based generative model. We have implemented it for an RNN using the same principle to compare it to the other strategies used here as another baseline strategy. This mechanism keeps track of the best agent so far, updating it periodically. During optimization, a batch of molecules m (of batch size S) is sampled from both the current agent (𝑀 𝑎𝑔𝑒𝑛𝑡 ) and best agent (𝑀 𝑏𝑒𝑠𝑡 ), to serve as a reminder of high scoring molecules. Although the loss function is the same as Equation for the respective agents, the loss weighted average is taken across agents scaled by α, as shown in Equation . This effectively acts to minimize the agent policy difference to the 'best agent optimal policy' and the 'prior optimal policy', scaled by α.