aiqbench analysis
This paper introduces LLaVA-CKD, a bottom-up cascaded knowledge distillation framework designed to optimize the deployment of Large Vision-Language Models by bridging capacity gaps between teacher and student networks. By employing intermediate teachers inspired by progressive education systems, the method achieves competitive performance on Visual Question Answering benchmarks while significantly reducing memory and compute requirements. This research offers a practical pathway for deploying efficient VLMs in resource-constrained environments without sacrificing accuracy.
LLaVA-CKD: Bottom-Up Cascaded Knowledge Distillation for Vision-Language Models
arXiv:2605.10641v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (VLMs) are successful in addressing a multitude of vision-language understanding tasks, such as Visual Question Answering (VQA), but their memory and compute requirements remain a concern for practical deployment. A promising class of techniques for mitigating this concern is Knowledge Distillation, where knowledge from a high-capacity Teacher network is transferred to a considerably smaller Student network. However, the capacity gap between the two networks is both a blessing and a curse: the smaller the Student network, the better its efficiency, and the stronger (typically, also larger) the Teacher, the more knowledge it carries; yet, beyond a point, the larger capacity gap between the two leads to worse knowledge transfer. To counter this effect, we propose a bottom-up cascaded knowledge distillation (CKD) framework. Instead of treating knowledge transfer as an activity involving one high-capacity Teacher (or an ensemble of such), inspired by human formal education systems, we introduce one (potentially, more) additional Teacher(s) of intermediate capacity that gradually bring the Student network to the next level, where the next (higher-capacity) Teacher can take over. We provide a theoretical analysis to motivate why progressive reduction of the Teacher-Student gap may improve distillation. We apply the proposed framework on models built upon the LLaVA methodology and evaluate the derived models on seven standard, publicly available VQA benchmarks, demonstrating competitive performance with recent compact VLMs. Source code for cascaded distillation and trained models are available at https://github.com/IDT-ITI/LLaVA-CKD.
Abstract:Large Vision-Language Models (VLMs) are successful in addressing a multitude of vision-language understanding tasks, such as Visual Question Answering (VQA), but their memory and compute requirements remain a concern for practical deployment. A promising class of techniques for mitigating this concern is Knowledge Distillation, where knowledge from a high-capacity Teacher network is transferred to a considerably smaller Student network. However, the capacity gap between the two networks is both a blessing and a curse: the smaller the Student network, the better its efficiency, and the stronger (typically, also larger) the Teacher, the more knowledge it carries; yet, beyond a point, the larger capacity gap between the two leads to worse knowledge transfer. To counter this effect, we propose a bottom-up cascaded knowledge distillation (CKD) framework. Instead of treating knowledge transfer as an activity involving one high-capacity Teacher (or an ensemble of such), inspired by human formal education systems, we introduce one (potentially, more) additional Teacher(s) of intermediate capacity that gradually bring the Student network to the next level, where the next (higher-capacity) Teacher can take over. We provide a theoretical analysis to motivate why progressive reduction of the Teacher-Student gap may improve distillation. We apply the proposed framework on models built upon the LLaVA methodology and evaluate the derived models on seven standard, publicly available VQA benchmarks, demonstrating competitive performance with recent compact VLMs. Source code for cascaded distillation and trained models are available at this https URL.
IEEE Access, vol.14, 2026
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2605.10641 [cs.CV]
(or arXiv:2605.10641v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.10641 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1109/ACCESS.2026.3744200 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 350 words