aiqbench analysis
This research demonstrates a highly efficient method for distilling advanced reasoning capabilities into compact 2B-parameter video-language models using synthetic chain-of-thought data. By strategically placing CoT rationales after the answer, the approach enables smaller models to outperform significantly larger counterparts with minimal compute resources, offering a viable path toward deploying sophisticated AI on edge devices.
Efficient Reasoning Distillation: Small Video-Language Models via Synthetic CoT and Difficulty-Aware Fine-Tuning
arXiv:2609.16255v1 Announce Type: cross Abstract: We present an efficient method to distill reasoning capabilities into compact video-language models (VLMs) for video question answering (VideoQA). Our approach fine-tunes a 2B-parameter model using only $\sim$900 uncertainty-selected examples, each augmented with synthetic chain-of-thought (CoT) rationales generated by a 4B teacher. Despite its minimal compute cost - under two hours on a single A100 GPU - our method enables the 2B model to outperform VLMs up to 4$\times$ larger, and generalize across CinePile, ActivityNet-QA, and MLVU, approaching the performance of its own 4B teacher. A key finding is that placing CoT rationales after the answer - contrary to standard prompting - substantially improves reasoning in compact models. This insight challenges prevailing CoT conventions and reveals new alignment strategies under limited model capacity. Our findings offer a practical blueprint for training deployable, reasoning-rich VLMs suited for mobile and edge applications.
Abstract:We present an efficient method to distill reasoning capabilities into compact video-language models (VLMs) for video question answering (VideoQA). Our approach fine-tunes a 2B-parameter model using only $\sim$900 uncertainty-selected examples, each augmented with synthetic chain-of-thought (CoT) rationales generated by a 4B teacher. Despite its minimal compute cost - under two hours on a single A100 GPU - our method enables the 2B model to outperform VLMs up to 4$\times$ larger, and generalize across CinePile, ActivityNet-QA, and MLVU, approaching the performance of its own 4B teacher. A key finding is that placing CoT rationales after the answer - contrary to standard prompting - substantially improves reasoning in compact models. This insight challenges prevailing CoT conventions and reveals new alignment strategies under limited model capacity. Our findings offer a practical blueprint for training deployable, reasoning-rich VLMs suited for mobile and edge applications.
14 pages, 2 figures, 5 tables. Published in MultiMedia Modeling (MMM 2026), LNCS 16412
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2609.16255 [cs.LG]
(or arXiv:2609.16255v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.16255 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
MultiMedia Modeling (MMM 2026), Lecture Notes in Computer Science, vol. 16412, pp. 567-580, Springer, Singapore, 2026
https://doi.org/10.1007/978-981-95-6950-2_40 Focus to learn more DOI(s) linking to related resources
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