Empowering Small VLMs to Think with Dynamic Memorization and Exploration

Abstract

Small-scale Vision-Language Models (SVLMs) are exceptionally well-suited for proprietary tasks. Equipping them with thinking capabilities is a critical step to enhance their performance and reliability in these specific domains. However, existing training paradigms, including Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Reward (RLVR), impose substantial demands on the base VLM, exceeding the capacity of SVLMs. Consequently, directly applying these paradigms to SVLMs fails to instill the desired thinking abilities. A natural solution is to combine SFT and RLVR, leveraging their complementarity to reduce the dependence on model capacity. Yet the core challenge lies in managing the inherent trade-off: excessive reliance on SFT can force the model to memorize pseudo thinking traces, while over-emphasizing RLVR can lead to unstable exploration (*i.e.,* advantage collapse). To address this, we propose *DyME*, a novel training paradigm that **Dy**namically selects between **M**emorization (via SFT) and **E**xploration (via RLVR) at each optimization step, ensuring that every update contributes to the trade-off. By ensuring that every update contributes to the trade-off, *DyME* serves as a robust, standalone strategy that stabilizes SVLM learning. Complementing this paradigm, we further introduce a synergistic \textit{Visual Supervision} mechanism (comprising a visual checker and refiner) designed to inject dynamically enhanced, image-grounded guidance during optimization. Extensive experiments across diverse domains demonstrate that *DyME* consistently achieves this balance, and thus delivers substantial performance improvements on specialized tasks. These results establish *DyME* as a practical and effective solution for empowering SVLMs with reliable thinking capabilities.