We study label-efficiency in RF modulation recognition by co-training a small temporal CNN with a stack of classicalmodels (RF, SVM, GBM, KNN) using handcrafted features. Withonly 0.5% ∼ 10% labels, co-training yields consistent AUROCgains and improved robustness under test-time SNR shifts. Codeand figures are fully reproducible.