Previous work has explored RL strategies and parameters for RNNs de novo molecule generation to varying degrees. Niel et al. compared different RL strategies (including REINFORCE, HC and REINVENT) and optimized a selection of tasks. However, the difference in sample-efficiency was not clear and their code was not published. A comparison of REINVENT versions 1.0 and 2.0 shows that the default sigma parameter value was increased. This effectively increases the reward contribution compared to the prior contribution and theoretically improves sample-efficiency, although this was not discussed in the publication . Fialková et al. investigated more significant modifications to the REINVENT loss function which did not result in any significant improvement. Meanwhile, Atance et al. modified the loss function by adding a best agent reminder (BAR) mechanism to the loss function resulting in 'significantly improved learning' (although this was not further quantified by the authors and it pertained to use on a graph-based generation model).