Researchers at Hugging Face have achieved a long-elusive breakthrough in model compression, demonstrating that heavily quantized 4-bit language models can outperform their full-precision 16-bit counterparts. When applied to a GPT-OSS model compressed from 120B to 60B parameters and quantized to MXFP4, the resulting 4-bit version surpassed the full-precision baseline on 7 of 9 standard benchmarks, marking a reversal of the traditional efficiency-accuracy tradeoff that has constrained AI deployment for years. The innovation, published in a paper titled "Quantization-Aware Healing," addresses a critical gap that previous compression techniques have struggled to overcome. Earlier approaches like quantization-aware training and quantization-aware distillation face fundamental limitations when models undergo both structural compression and quantization simultaneously. Quantization-Aware Healing sidesteps these constraints by distilling directly from the original full-size model rather than from the smaller recovered checkpoint, eliminating the accuracy ceiling that previously capped quantized performance. To implement QAH at scale and support long contexts up to 32,000 tokens, the researchers developed a memory-efficient chunked KL-divergence loss that fits within standard GPU memory budgets. The technique reframes quantization not as a lossy postprocessing step but as a second, comprehensive distillation pass that transfers information the initial recovery stage could not access.