Efficient AI Models for Improved Reasoning Abilities: A Look at InfiR

Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have made impressive progress in the field of reasoning abilities in recent years. However, they continue to face challenges, particularly regarding their high computational demands and associated costs, as well as concerns about data privacy. A new research approach therefore focuses on the development of efficient Small Language Models (SLMs) and Multimodal Small Language Models (MSLMs) that offer competitive reasoning abilities despite their smaller size.

InfiR: An Innovative Approach for Compact AI Models

The InfiR project introduces a novel training pipeline aimed at improving the reasoning abilities of SLMs and MSLMs while enabling deployment on resource-constrained devices, such as edge devices. This approach promises to achieve state-of-the-art performance at minimal development costs.

The motivation behind InfiR lies in improving AI systems by strengthening reasoning abilities, reducing entry barriers for using the technology, and addressing data privacy concerns through the use of smaller model sizes. The smaller size of the models reduces the need for computing power and storage space, making the models suitable for use on devices with limited resources. This opens up new possibilities for applications in the field of edge computing, where data is processed directly on-site without relying on a cloud infrastructure.

Training and Performance

The training pipeline used in InfiR specifically optimizes the models for reasoning tasks. Details on the training process, the datasets used, and the results achieved are documented in the associated scientific publication. The research results indicate that InfiR achieves improved performance in reasoning tasks compared to existing approaches.

Potential Applications and Future Developments

The development of efficient SLMs and MSLMs with strong reasoning abilities opens up a variety of application possibilities. Due to the lower requirements for computing power and storage space, these models can be used in areas where the use of LLMs has not been practical so far, such as in mobile applications, embedded systems, or in the field of the Internet of Things (IoT).

Future research work could focus on further optimizing the training pipeline, expanding the model functionality, and exploring new areas of application. The development of smaller and more efficient AI models is an important step towards wider availability and use of artificial intelligence.

Bibliography: - Xie, C., et al. "InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning." arXiv preprint arXiv:2502.11573 (2025). - https://arxiv.org/abs/2502.11573 - https://arxiv.org/html/2502.11573v1 - https://huggingface.co/papers/2502.11573 - https://huggingface.co/papers - https://2024.aclweb.org/program/main_conference_papers/ - https://www.superannotate.com/blog/small-language-models - https://github.com/dair-ai/ML-Papers-of-the-Week - https://aclanthology.org/2024.findings-acl.924.pdf - https://github.com/Yangyi-Chen/Multimodal-AND-Large-Language-Models