UniCompress: From Multi-LoRA Experts to One Compact Model for Edge Intelligence

Authors: Mingkang Liu , Peipei Jiao , Weiming Meng , Tianyuan Liu , Lichen Pang , Junyan Li

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Abstract: Embodied and edge systems increasingly rely on vision-language models (VLMs) to support heterogeneous skills, yet deploying a separate task adapter for each skill while retaining a high-precision backbone creates substantial memory and management costs. We present UniCompress, a deployment-oriented framework that converts multi-task expertise into one compact, adapter-free model. Starting from a consolidated BF16 Qwen3-VL-4B teacher, UniCompress constructs an asymmetric INT2 student using GPTQ initialization and teacher-guided quantization recovery. The approach jointly quantizes and recovers the projection weights in the vision encoder and language backbone, enabling aggressive compression of the complete vision-language model while preserving its multi-task behavior. We evaluate UniCompress on four heterogeneous embodied tasks: posture recognition, household-safety understanding, multimodal dialogue routing, and mobile-GUI action prediction. UniCompress reduces the approximate weight storage from 8.9 GB in BF16 to 2.1 GB in INT2, corresponding to a reduction of approximately 76%. It improves the INT2 macro task score by 34.3 percentage points over GPTQ-INT2 while remaining within 0.9 points of the consolidated BF16 teacher. These results demonstrate the potential of teacher-guided joint vision-language INT2 recovery for deploying a single multi-skill VLM on resource-constrained embodied platforms.

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2611.06075 [cs.AI]

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arXiv:2611.06075v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.06075

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arXiv-issued DOI via DataCite (pending registration)

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From: Lichen Pang [ view email ]

[v1]

Wed, 4 Nov 2026 07:18:55 UTC (182 KB)

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