Presents PyGeoX DSL and 300-problem benchmark, identifies outlier gradient masking under global rewards, and shows Saturating Additive Rewards improve hard-tier solving rate by 2.3x with an 8B model competitive to larger systems.
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Internalizing Geometric Law: Learning from Solver Residuals for Precision-Critical Generation
Presents PyGeoX DSL and 300-problem benchmark, identifies outlier gradient masking under global rewards, and shows Saturating Additive Rewards improve hard-tier solving rate by 2.3x with an 8B model competitive to larger systems.