Neuro-Symbolic Synergy for World Modeling
NeSyS alternates training between the two models using trajectories inadequately explained by the other, and prioritizes transitions that are weakly covered by symbolic rules, using 35--60% of the training pairs while outperforming full-data supervised fine-tuning in five of six settings.
Hongyu Zhao, Siyuan Zhou, Hao-Lin Yang et al.
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