Multimodal benchmarks, datasets, and selective training strategies to improve multimodal reasoning

In 2026, the field of multimodal AI has experienced transformative advancements, emphasizing the construction of high-quality datasets, the development of robust benchmarks, and innovative training strategies to enhance multimodal reasoning capabilities.

Construction and Use of High-Quality Multimodal Datasets and Benchmarks

Central to progress in multimodal reasoning is the availability of comprehensive and diverse datasets. The release of DeepVision-103K, a dataset encompassing over 103,000 samples across visual, textual, and mathematical domains, exemplifies efforts to provide broad coverage and verifiable data for training large models. Such datasets facilitate the development of models capable of understanding and reasoning across multiple data types, breaking down previous barriers that limited cross-modal integration.

In addition, research on construction methods for high-quality multimodal datasets has gained momentum, focusing on ensuring data relevance, diversity, and annotation quality. These efforts underpin the creation of benchmarks like SAW-Bench, which evaluates a system’s situational awareness and reasoning across modalities, pushing models toward more sophisticated understanding.

Training Schemes Focusing on Informative Visual Data and Unified Reasoning

Innovative training strategies are pivotal in leveraging these datasets effectively. Techniques such as Selective Training for Large Vision-Language Models employ metrics like Visual Information Gain to identify the most informative visual samples, allowing models to prioritize high-impact data. This approach reduces training costs while maintaining or improving performance, addressing the challenge of vast data requirements in multimodal learning.

Furthermore, the development of unified multimodal models—like the LaViDa-R1 diffusion language model—integrates supervised fine-tuning with multimodal reasoning capabilities, allowing models to perform complex tasks that involve reasoning across text, images, and other data types. These models are designed to handle multi-step reasoning tasks, often utilizing chain-of-thought prompting strategies adapted for multimodal inputs, such as those discussed in UniT: Unified Multimodal Chain-of-Thought Test-time Scaling.

Recent articles, including Research on Construction Methods of High-Quality Multimodal Datasets and VLANeXt: Recipes for Building Strong VLA Models, emphasize best practices in dataset curation and model training, ensuring that models not only learn from high-quality data but also reason effectively across modalities.

Emerging Techniques and Future Directions

Emerging techniques like vector graphics grounding (e.g., SVG encoding) and multimodal fact-level attribution are enhancing models' ability to generate, manipulate, and verify multimodal content with greater fidelity and explainability. For instance, models trained with fact-level attribution can trace outputs back to input evidence, increasing trustworthiness—an essential feature for applications in scientific, medical, and security domains.

Additionally, the integration of diagnostic benchmarks and intervention frameworks supports the systematic evaluation and improvement of cross-modal reasoning, ensuring models are robust and scalable in real-world settings.

Conclusion

Overall, 2026 marks a pivotal year where the synergy of high-quality datasets, sophisticated training schemes focusing on informative visual data, and unified multimodal reasoning models propel the field forward. These innovations lay the groundwork for AI systems capable of complex, multi-step reasoning across diverse data streams, opening new horizons in autonomous reasoning, virtual environments, and intelligent systems that understand and operate seamlessly across modalities.