OJBKQ: Objective-Joint Babai-Klein Quantization

• URL: http://arxiv.org/abs/2602.08376v1
• Date: Mon, 09 Feb 2026 08:20:12 GMT
• Title: OJBKQ: Objective-Joint Babai-Klein Quantization
• Abstract summary: OJBKQ is a layer-wise PTQ method that formulates weight quantization as a joint optimization problem over activations and weights.<n>Results on large language models show that OJBKQ achieves lower perplexity at 3-4 bits compared to existing PTQ approaches.
• Score: 13.930991942645276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Post-training quantization (PTQ) is widely used to compress large language models without retraining. However, many existing weight-only methods rely on heuristic objectives and greedy rounding, thus leading to noticeable degradation under low-bit quantization. In this work, we introduce OJBKQ (Objective-Joint Babai-Klein Quantization with K-Best Sampling), a layer-wise PTQ method that formulates weight quantization as a joint optimization problem over activations and weights. This formulation results in a multiple-right-hand-side box-constrained integer least squares (BILS) problem in each layer, which is NP-hard. For each column of the weight matrix, we apply an extended Babai nearest-plane algorithm and an extended version of Klein's randomized Babai algorithm to find the minimum-residual Babai-Klein point, a sub-optimal solution to the BILS problem. Experimental results on large language models show that OJBKQ achieves lower perplexity at 3-4 bits compared to existing PTQ approaches, while maintaining comparable computational cost.

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