In a controlled arithmetic-grammar program synthesis environment, diverse sampling across semantic and syntactic spaces yields robust density generalization while support generalization for novel syntax remains poor, with performance falling over 30 percent and compute scaling following a strictly 1
Smith, Mateusz Paprocki, Ondřej Čertík, Sergey B
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Beyond the Training Distribution: Mapping Generalization Boundaries in Neural Program Synthesis
In a controlled arithmetic-grammar program synthesis environment, diverse sampling across semantic and syntactic spaces yields robust density generalization while support generalization for novel syntax remains poor, with performance falling over 30 percent and compute scaling following a strictly 1