GenRecal: Generation after Recalibration from Large to Small Vision-Language Models (2506.15681v1)

Abstract: Recent advancements in vision-LLMs (VLMs) have leveraged LLMs to achieve performance on par with closed-source systems like GPT-4V. However, deploying these models in real-world scenarios, particularly on resource-constrained devices, remains challenging due to their substantial computational demands. This has spurred interest in distilling knowledge from large VLMs into smaller, more efficient counterparts. A key challenge arises here from the diversity of VLM architectures, which are built on different LLMs and employ varying token types-differing in vocabulary size, token splits, and token index ordering. To address this challenge of limitation to a specific VLM type, we present Generation after Recalibration (GenRecal), a novel, general-purpose distillation framework for VLMs. GenRecal incorporates a Recalibrator that aligns and adapts feature representations between heterogeneous VLMs, enabling effective knowledge transfer across different types of VLMs. Through extensive experiments on multiple challenging benchmarks, we demonstrate that GenRecal significantly improves baseline performances, eventually outperforming large-scale open- and closed-source VLMs.

• The paper introduces the GenRecal framework that uses a Recalibrator to align heterogeneous feature representations between large and small vision-language models.
• Experimental validations on benchmarks like MM-Vet and MMMU demonstrate significant performance improvements over traditional distillation methods.
• The framework reduces computational demands, broadening the practical utility of VLMs for real-time applications in robotics, healthcare, and mobile devices.

Generation after Recalibration: A Framework for Efficient Vision-LLM Distillation

The paper "GenRecal: Generation after Recalibration for Large to Small Vision-LLMs" presents a novel framework for the distillation of Vision-LLMs (VLMs), addressing significant challenges in deploying large VLMs on resource-constrained devices. The emergence of robust VLMs, which integrate extensive LLMs to process multimodal information effectively, has been transformative in tasks such as image captioning and visual question answering. Despite their advancements, the high computational demands of VLMs hinder their applicability in environments with limited resources. This has propelled research into distilling knowledge from large VLMs into smaller, more efficient models, yet doing so poses a challenge due to the diversity in the architectures and token types of VLMs.

GenRecal Framework

The authors introduce "Generation after Recalibration" (GenRecal), a versatile distillation framework designed to transcend architectural and token-type limitations in VLMs. A cornerstone of this framework is the Recalibrator, which aligns and adapts feature representations between heterogeneous VLMs. This adaptation is crucial as traditional distillation methods falter when large and small VLMs do not share the same vocabulary sizes, token splits, or token index ordering schemes. GenRecal's Recalibrator facilitates the effective transfer of knowledge across different VLM types, thereby enabling distillation irrespective of inherent architectural differences.

Experimental Validation

The paper reports extensive experiments to validate the efficacy of GenRecal. The results underscore that GenRecal not only improves baseline performances but also outperforms existing large-scale open and closed VLM sources. These experiments were conducted on challenging benchmarks, including MM-Vet and MMMU, showcasing GenRecal's superior ability to facilitate knowledge transfer. Particularly noteworthy is the framework's robust performance in distilling knowledge from advanced VLMs, such as InternVL2.5, into smaller models like Qwen2-VL-7B, with consistent enhancements over traditional methods like LLaVA-KD.

Implications and Future Directions

From a practical standpoint, GenRecal offers a significant reduction in computational demands for VLM deployment, making potent AI technologies accessible to a broader array of devices and applications. This democratization of AI capability can be particularly impactful for real-time visual understanding tasks in fields such as robotics, healthcare, and mobile applications. Theoretically, the framework paves the way for more flexible model architectures, encouraging the exploration of diverse approaches to multimodal learning that were previously constrained by strict architectural compatibility requirements.

Future research could explore several exciting directions building on GenRecal's foundation. These include the integration of multi-teacher distillation approaches, development of intermediate-layer recalibration techniques, and reinforcement of cross-architecture alignment strategies to further enhance the flexibility and efficiency of AI systems. Moreover, the potential to leverage GenRecal for continuous learning scenarios, where models need to be periodically updated with new data without extensive retraining, represents another promising avenue.

In conclusion, the GenRecal framework marks a significant step in the evolution of VLM distillation, presenting a scalable method to transfer knowledge across varied model types efficiently. As AI models continue to grow in complexity and capability, frameworks like GenRecal will be crucial in maximizing their utility across diverse application domains.