In a breakthrough that challenges conventional wisdom in artificial intelligence, Samsung has unveiled a Tiny Recursive Model (TRM) with only 7 million parameters. Despite its minuscule size, this model has outperformed many of the industry's largest language models—some more than 10,000 times bigger—on a range of challenging reasoning and puzzle-solving benchmarks.
Key Takeaways
Samsung’s Tiny Recursive Model (TRM) achieves state-of-the-art results in reasoning tasks, despite its minimal size.
Outperforms leading AI models—like Google's Gemini 2.5 Pro and OpenAI’s o3-mini—on structured problems, including Sudoku and ARC-AGI benchmarks.
Offers a path toward more sustainable, accessible, and efficient AI research beyond the "bigger is better" arms race.
Small Size, Big Performance
While most tech industry efforts focus on ever-larger language models with billions or even trillions of parameters, Samsung’s TRM stands out by achieving high-level performance with only 7 million parameters. This is less than 0.01% of the size of most leading models.
TRM was rigorously tested against a suite of demanding reasoning challenges. On the Sudoku-Extreme benchmark, it scored 87% accuracy—leapfrogging much larger models. It also performed impressively on the Maze-Hard and ARC-AGI datasets, benchmarks designed to assess abstract reasoning and problem-solving capabilities:
The Secret: Recursive Reasoning
Rather than relying on scale, TRM utilises a clever recursive process that mirrors human behaviour—re-reading and refining answers. At each step, the model reviews its current solution and reasoning, then attempts to improve upon it. This cycle can be repeated up to 16 times, effectively deepening the reasoning process without adding more parameters or layers.
Interestingly, Samsung found that keeping the model lean—just two neural network layers—actually improved its ability to generalise across tasks, as increasing layers led to overfitting.
Cost-Effective and Accessible AI
One of the significant implications of Samsung’s achievement is the accessibility and efficiency of advanced AI. Running large models demands specialised hardware and substantial energy, restricting experimentation to major corporations or institutions. TRM, with its limited size, can be trained and deployed on standard hardware at a fraction of the cost, opening up research possibilities for universities, startups, and independent developers.
A New Direction for AI Research
While TRM does not replace large-scale language models for open-ended tasks, its exceptional performance on structured grid problems challenges the notion that increasing scale is the only path to progress. It demonstrates that intelligent model design can deliver outstanding results and may influence future research on specialised, sustainable AI systems.
As AI innovation accelerates, Samsung’s TRM stands as evidence that sometimes, less truly is more.