Quantization-Aware Healing: a compressed, 4-bit model that outperforms its full-precision original

Multiverse Computing presents Quantization-Aware Healing (QAH), a recovery method for language models that have undergone both structural compression and 4-bit quantization, processes that typically reduce reasoning, mathematics, coding, and other capabilities. Rather than distilling a compressed model from its recovered full-precision version, QAH uses the original pre-compression model as a frozen teacher and trains the quantized student to match its output distributions through KL-divergence loss, including a memory-efficient chunked implementation for contexts up to 32,000 tokens. Applied to a GPT-OSS 120B model compressed to 60B parameters and quantized to MXFP4, the method reportedly outperformed the corresponding recovered 60B bfloat16 checkpoint on seven of nine benchmarks, with particularly large gains in long-context reasoning and math, while also exceeding the original 120B teacher on LiveCodeBench. In a separate comparison with quantization-aware training, QAH achieved a similar peak score substantially faster and remained more stable after reaching it, whereas the alternative declined sharply with extended training. The authors argue that QAH can make compressed 4-bit models smaller, less expensive to run, and in some cases more accurate than their full-precision compressed counterparts.

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