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Larger RLVR batches, despite appearing efficient with square-root learning rates, suffer a critical batch size that degrades sample efficiency and hastens failure due to curvature effects.
Aesthetic code generation is unlocked by using a VLM judge to translate visual design principles from a reference image into actionable rewards for code models.
