JustQuant: You Don’t Need Smoothing, SVD, or Rotation for 4-Bit Activation Quantization

Recent generative models have become increasingly powerful, but their inference cost continues to grow. Model quantization is a promising path for compressing these models and accelerating inference. However, when quantization is pushed to 4 bits, activation quantization becomes substantially more difficult than weight quantization. Recent PTQ (post-training quantization) and QAT (quantization-aware training) methods have made progress on 4-bit activation quantization by introducing smoothing, SVD branches, rotations, mixed precision, or advanced formats such as NVFP. Yet these operators and data types impose demanding requirements on inference engines and hardware, limiting the broad adoption of low-precision models. **Can we quantize using only plain low-bit operators?** To answer this question, we propose **JustQuant**, a simple yet effective framework that moves the complexity required for low-bit quantization from deployment-side operators into an acceptable training process. First, we revisit model quantization from the perspective of knowledge distillation, and show that a key reason existing PTQ and QAT methods fail is that they typically exploit supervision at only a single level. We then introduce Theseus QAD (quantization-aware distillation), a quantization-aware distillation method that progressively applies multi-level supervision, analogous to the gradual replacement process in the Ship of Theseus. Extensive experiments on DiT and dLLM show two regimes. For smaller models, can serve as a light warm-up stage that substantially improves QAT with plain operators, while naive QAD can collapse in the same setting. For larger models, Theseus QAD provides a stronger distillation training path than ordinary QAD. Across these regimes, Theseus QAD improves plain quantization while avoiding the complex operators required by many PTQ methods.