Negative result for sparse predictive hierarchies on chess

A study compared sparse predictive hierarchies (SPH) based on Ogma's whitepapers against a standard backprop CNN on Lc0 chess data. The 44.9M-parameter SPH model badly trailed a 3.8M CNN on policy accuracy and failed to converge, suggesting a steep penalty for extreme sparsity with local learning.