LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation

• URL: http://arxiv.org/abs/2604.19167v1
• Date: Tue, 21 Apr 2026 07:25:02 GMT
• Title: LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
• Abstract summary: We present LBLLM, a lightweight binarization framework that achieves W(1+1)A4 quantization through a novel three-stage quantization strategy.<n>LBLLM, trained only using 0.016B tokens with a single GPU, surpasses existing state-of-the-art binarization methods on W2A4 quantization settings.
• Score: 29.676785178178104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying large language models (LLMs) in resource-constrained environments is hindered by heavy computational and memory requirements. We present LBLLM, a lightweight binarization framework that achieves effective W(1+1)A4 quantization through a novel three-stage quantization strategy. The framework proceeds as follows: (1) initialize a high-quality quantized model via PTQ; (2) quantize binarized weights, group-wise bitmaps, and quantization parameters through layer-wise distillation while keeping activations in full precision; and (3) training learnable activation quantization factors to dynamically quantize activations to 4 bits. This decoupled design mitigates interference between weight and activation quantization, yielding greater training stability and better inference accuracy. LBLLM, trained only using 0.016B tokens with a single GPU, surpasses existing state-of-the-art binarization methods on W2A4 quantization settings across tasks of language modeling, commonsense QA, and language understanding. These results demonstrate that extreme low-bit quantization of LLMs can be both practical and highly effective without introducing any extra high-precision channels or rotational matrices commonly used in recent PTQ-based works, offering a promising path toward efficient LLM deployment in resource-limited situations.

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