[Submitted on 23 Sep 2026]
Title:Small yet Assistive: Spatially-Aware Post-Training for Low Vision
View a PDF of the paper titled Small yet Assistive: Spatially-Aware Post-Training for Low Vision, by Rishabh Choudhary and 8 other authors
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Abstract:An estimated 1 billion people worldwide live with vision impairment, yet current vision-language models (VLMs) produce descriptions too vague for safe navigation by blind and low-vision (BLV) users. Large VLMs can generate high-quality audio-description-compliant narrations but cannot run on mobile devices; small VLMs offer competitive latency but lack spatial detail, directional cues, and hazard awareness for navigational assistance. We present Smol-VL-BLV, a compact VLM for blind and low-vision users that closes this gap using a 500M decoder transformer model and two post-training mechanisms: (1) teacher-student distillation and (2) Group Relative Policy Optimization (GRPO) with a composite BLV reward targeting directional language, metric distances, and hazard detection. Because multi-stage post-training can induce catastrophic forgetting, we add a lightweight finetuning stage after the last stage GRPO finetuning to recover general descriptive quality while preserving BLV-specific spatial grounding. Our best model substantially outperforms the baseline across various benchmarks, including tasks: VQA, BLV captioning, OCR, and latency. Compared with the baseline for relative improvement, it improves the Spatial score gain of 19.3%, and the Social score gain of 14.8%. It also increases OCR-Bench by 101.5%, and raises TextVQA accuracy by 44.2%. These results show that BLV-focused post-training improves both accessibility-specific spatial grounding and general visual-text reasoning. Deployed on a mid-range Android smartphone via Mixed-Precision Quantization, the model remains approx. 450 MB and runs entirely on-device, offline and without network dependency, generating descriptions with latency dependent on host hardware capabilities. Our model, dataset, and code is publicly released at this https URL
Comments: 14 pages, Accepted in EMNLP 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
MSC classes: 68T45
ACM classes: I.2.10
Cite as: arXiv:2609.28757 [cs.CV]
(or arXiv:2609.28757v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.28757
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sushovan Jena [view email] [v1] Wed, 23 Sep 2026 20:10:25 UTC (4,813 KB)
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