The trend of AI researchers developing new, small open source generative models that outperform far larger, proprietary peers continued this week with yet another staggering advancement.Alexia Jolicoeur-Martineau, Senior AI Researcher at Samsung’s Advanced Institute of Technology (SAIT) in Montreal, Canada, has introduced the Tiny Recursion Model (TRM) — a neural network so small it contains just 7 million parameters (internal model settings), yet it competes with or surpasses cutting-edge language models 10,000 times larger in terms of their parameter count, including OpenAI’s o3-mini and Google’s Gemini 2.5 Pro, on some of the toughest reasoning benchmarks in AI research. The goal is to show that very highly performant new AI models can be created affordably without massive investments in the graphics processing units (GPUs) and power needed to train the larger, multi-trillion parameter flagship models powering many LLM chatbots today. The results were described in a research paper published on open access website arxiv.org, entitled “Less is More: Recursive Reasoning with Tiny Networks.””The idea that one must rely on massive foundational models trained for millions of dollars by some big corporation in order to solve hard tasks is a trap,” wrote Jolicoeur-Martineau on the social network X. “Currently, there is too much focus on exploiting LLMs rather than devising and expanding new lines of direction.”Jolicoeur-Martineau also added: “With recursive reasoning, it turns out that ‘less is more’. A tiny model pretrained from scratch, recursing on itself and updating its answers over time, can achieve a lot without breaking the bank.”TRM’s code is available now on Github under an enterprise-friendly, commercially viable MIT License — meaning anyone from researchers to companies can take, modify it, and deploy it for their own purposes, even commercial applications.One Big CaveatHowever, readers should be aware that TRM was designed specifically to perform well on structured, visual, grid-based problems like Sudoku, maze …
