In particular, these RL-based methods are extremely sensitive to their reward functions, where a little variation may lead to a significant fluctuation of the reasoning performance.
for different datasets, the reward functions in the RL-based methods need manual adjustments to achieve a good performance, which will not only be inefficient and laborious but also difficult to adapt to the rapid evolutions of realworld KGs (Shi and Weninger, 2018).