FreqExit: Enabling Early-Exit Inference for Visual Autoregressive Models via Frequency-Aware Guidance 1 2 1 Ying Li Chengfei Lv Huan Wang 1Westlake University 2Alibaba Group Original VarFigure

FreqExit is based on a key insight: high-frequency details are crucial for perceptual quality and tend to emerge only in later decoding stages. Leveraging this insight, we design targeted mechanisms that guide the model to learn more effectively through frequency-aware supervision. The proposed framework consists of layer three dropout components: and early (1) e a xit curriculum-based loss; (2) a wav supervision elet-domain strate high-frequenc gy with progressi y consisve tency loss that aligns spectral content across different generation steps; and (3) a lightweight self-supervised frequency-gated module that guides adaptive learning of both structural and detailed spectral components. On ImageNet 256 256, FreqExit achieves up to 2 speedup with only minor degradation, and delivers 1.3 acceleration without perceptible quality loss. This enables runtime-adaptive acceleration able trade-of within f between a consistent efficiency design and fidelity tailored for for practica next-scale l and VAR, flexible offering deplo a yment.