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.
Figures & tables
Figure 1: Overview of UniCompress. Stage 1 consolidates four task-specific LoRA experts into a single Unified-BF16 teacher. Stage 2 trains a deployment-faithful INT2 student with offline anchoring and on-policy recovery, where the floating-point shadow policy assigns token weights according to quantization sensitivity. Only the INT2 student is deployed.
Task
Train
Test
Primary Metric
SPM
7,353
294
3-class accuracy
HSM
15,723
751
Label-set accuracy
TCR
11,025
500
Turn accuracy
MGC
58,415
2,062
Exact-step accuracy
Train denotes effective examples in one interleaved epoch.
Table 1: Statistics of the Four On-Device Tasks
Model
Size
SPM
HSM
TCR
MGC
Avg.
Multi-LoRA Experts a
∼ 8.9 GB + 0.2 GB adapters
94.9%
94.5%
97.6%
87.5%
93.6%
Unified-BF16 b
∼ 8.9 GB
95.2%
94.7%
97.2%
87.7%
93.7%
GPTQ-INT4
∼ 3.3 GB
93.2%
93.7%
96.8%
87.5%
92.8%
UniCompress-INT4
∼ 3.3 GB
95.2%
94.5%
96.6%
87.8%
93.5%
GPTQ-INT2
∼ 2.1 GB
71.8%
85.9%
58.6%
17.8%
58.5%
UniCompress-INT2
∼ 2.1 GB
93.2%
95.1%
97.4%
85.4%
92.8%
Table 2: Results Across the Two-Stage Consolidation and Quantization Pipeline
Setting
SPM
HSM
TCR
MGC
Avg.
Offline only
93.2%
92.5%
95.8%
83.9%
91.4%
OPD only
92.9%
93.7%
96.4%
84.7%
91.9%
UniCompress-INT2
93.2%
95.1%
97.4%
85.4%
92.8%
Table 3: Ablation Study of the INT2 Training Objectives
Figure 2: Weight storage versus macro-average score. UniCompress-INT2 lies on the Pareto frontier: it recovers 34.3 points over GPTQ-INT2 at the same 2.1 GB storage and reduces the weight storage by 76% relative to BF16 models.