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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.
Grounding audio language models with acoustic feature representations unlocks more accurate and explainable deepfake detection, even with smaller models.