EfficientQAT
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- ThinkBit proposes two-bit quantization for reasoning models
Rhea Sanjay Sukthanker and Pashmina Cameron submitted the ThinkBit paper to arXiv on October 26, 2026. The framework uses layer-specific bit allocation, Gumbel-softmax-based scalar quantization, scale recalibration and quantization-aware training to preserve reasoning while compressing weights to two bits. The authors report that a 0.52 GB Qwen3-1.7B checkpoint scored 46.7 on MATH-500 after recalibration and 73.0 after QAT, while a 3.74 GB model scaled to 14B scored 94.8 versus 97.5 for the 29.5 GB bfloat16 model; these results remain author claims from a single paper.
Hugging Face · Papers · 🔥 0
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