Samsung's Tiny Recursive Model (TRM) has only 7 million parameters, ten thousand times smaller than mainstream large models, yet it performs better on ARC-AGI 1 and 2.

It works differently. Large models predict the next word, while TRM first drafts an answer, then reasons on an internal "scratchpad," repeatedly critiquing and refining its logic for up to 16 cycles. Each cycle produces a better answer.

This proves that intelligence doesn't necessarily come from parameter scale; architecture and reasoning loops are equally crucial. Efficient models can run on inexpensive hardware, opening the door to high-quality reasoning for more applications.


Some are questioning if this is just overfitting or specialized for benchmarking. The authors responded with a Sudoku test: the model, trained on only 1,000 samples, achieved an 87.4% accuracy on 423,000 test puzzles, demonstrating a degree of generalization.


But there's a trade-off. A 7 million parameter model with 16 iterations versus a 70 billion parameter model with a single pass—which one wins on real-world latency? Efficiency could be an issue.

This forces a rethink of the path for AI development. The large model race might just be a competition for computational waste. Small, elegant solutions could be the future, especially those that can run locally on devices.

The paper and code are now public, and the community can verify it for themselves. Whether it's a breakthrough or just hype will soon be clear.