Properly spectral modeling within hyperspectral image (HSI) is critical yet highly challenging for HSI denoising. In contrast to existing methods that model long-range spectral dependencies with a huge cost and directly explore spatial-spectral information without region discrimination, we introduce RAS2S—a simple yet effective sequence-to-sequence (Seq2Seq) learning framework for better HSI denoising. RAS2S treats HSI denoising as a Seq2Seq translation problem, which converts the noisy spectral sequence to its clean ones in an autoregressive fashion. In addition, spatial-spectral information exploration without region discrimination contradicts the intrinsic spatial-spectral diversity of HSIs, leading to negative interference from spatial-spectral unrelated regions. Thus we propose a novel spatial-spectral region-aware module to distinctively perceive the semantic regions with different spatial-spectral representations, maximizing the spectral modeling potential of Seq2Seq learning. With such an improved Seq2Seq learning paradigm, RAS2S not only shows huge potential in capturing long-range spectral dependencies, but also maintains the flexibility to handle HSIs with arbitrary spectral numbers. Extensive experiments demonstrate that RAS2S outperforms existing state-of-the-art methods quantitatively and qualitatively with a minimal model size, merely 0.08M.