Matthew Berman, in his latest YouTube video, analyzes a groundbreaking paper titled "Less is More: Recursive Reasoning with Tiny Networks," focusing on a 7 million parameter model that challenges conventional wisdom in AI. This model, developed by a single author from Samsung SAIL, demonstrates superior performance on hard reasoning benchmarks compared to much larger models, including Gemini 2.5 Pro and DeepSeek, proving that size isn't everything.
The paper, authored by Alexia Jolicoeur-Martineau from Samsung SAIL Montreal, challenges the notion that larger language models (LLMs) are inherently better at complex reasoning tasks. The core insight is that the Tiny Recursive Model (TRM), with only 7 million parameters, achieves significantly higher generalization than Hierarchical Reasoning Models (HRM), which typically require more resources. Berman highlights the paradigm shift, stating, “This 7 million parameter model…is beating some of the top frontier models out there at the hardest reasoning benchmarks.”
One of the key revelations from Berman's analysis is the model's effectiveness in addressing the limitations of LLMs. Large language models struggle on hard question-answer problems because, as Berman explains, drawing from the paper, "they generate their answer auto-regressively, meaning they're simply predicting the next token.” This approach often leads to errors, as a single incorrect token can invalidate the entire answer.
This tiny AI model is poised to rewrite the playbook on what is possible with limited resources, showcasing that innovative architectures and novel training methodologies can compensate for sheer size. "We show that the benefit from recursive reasoning can be massively improved, making it much more than incremental," Berman quotes from the paper, underscoring the significance of this new approach. This statement is not merely an academic observation but a potential roadmap for future AI development.
The implications of this research extend beyond academic circles, hinting at a future where AI can be deployed on resource-constrained devices, from smartphones to embedded systems, without sacrificing performance. This has significant implications for industries ranging from defense and aerospace to consumer electronics.
Berman's video also touches on the practical applications of AI development, showcasing Mocha, an AI app builder that enables users with no coding experience to create fully functional applications. This tool democratizes AI development, allowing individuals to bring their ideas to life without the need for specialized technical skills.
