Samsung’s Tiny AI Model Outperforms Giant Reasoning LLMs

In a surprising turn for the artificial intelligence landscape, a new research paper from Samsung is challenging the prevailing “bigger is better” philosophy. Instead of relying on enormous Large Language Models (LLMs) bloated with parameters, Samsung’s AI researchers are demonstrating how a remarkably small network can achieve superior performance in complex reasoning tasks.

The research, spearheaded by Alexia Jolicoeur-Martineau at Samsung SAIL Montréal, unveils the Tiny Recursive Model (TRM), a model with a parameter count of just 7 million. This is less than 0.01% of the size of leading LLMs like those developed by Google and OpenAI. Yet, TRM is achieving state-of-the-art results on notoriously difficult AI benchmarks, including the challenging ARC-AGI (Abstraction and Reasoning Corpus) intelligence test.

This development throws a wrench into the gears of the AI arms race, where tech giants have been locked in a relentless pursuit of scale. Samsung’s work suggests a more sustainable and parameter-efficient alternative – one that could have significant implications for the future of AI development and deployment.

Overcoming the Limits of Scale

While LLMs have undoubtedly revolutionized natural language processing, their capacity for complex, multi-step reasoning often falters. Because they generate outputs sequentially, token-by-token, a single error early in the process can cascade, compromising the entire solution. This inherent vulnerability makes them less reliable when perfect logical execution is paramount.

Researchers have attempted to mitigate this issue using techniques like Chain-of-Thought, which encourages models to “think out loud” and break down complex problems into smaller, more manageable steps. However, Chain-of-Thought is computationally demanding, relies heavily on the availability of substantial amounts of high-quality reasoning data (which is often scarce), and can still produce flawed logic.

Samsung’s TRM builds upon the foundation of a recent AI model known as the Hierarchical Reasoning Model (HRM). HRM introduced a novel approach utilizing two small neural networks that recursively work on a problem at different frequencies to refine the answer iteratively. While the HRM showed initial promise, its implementation was complex. TRM takes a different approach.

Instead of two separate networks, TRM employs a single, compact network that recursively refines both its internal “reasoning” and its proposed “answer”. The model receives the question, an initial guess at the answer, and a latent reasoning feature. It then iteratively refines its latent reasoning based on these inputs. Using this improved reasoning, it updates its prediction for the final answer. This process can be repeated up to 16 times, empowering the model to progressively correct its own mistakes in a highly parameter-efficient manner.

One notable finding is that a two-layer network configuration exhibited superior generalization compared to a four-layer version. This suggests that reducing the model’s size can act as a safeguard against overfitting – a common pitfall when training on smaller, specialized datasets, as it can lead to improved model generalization and performance.

Furthermore, TRM avoids the complex mathematical underpinnings used by its predecessor, HRM. By implementing a different technique, TRM was able to significantly increase the accuracy on the Sudoku-Extreme benchmark from 56.5% to 87.4% in an ablation study.

Samsung’s Model Smashes AI Benchmarks with Fewer Resources

The empirical results are compelling. On the Sudoku-Extreme dataset, TRM achieved an 87.4% test accuracy, a marked improvement over HRM’s 55%. On Maze-Hard, a task requiring the discovery of long paths through 30×30 mazes, TRM attained an 85.3% score, surpassing HRM’s 74.5%.

Crucially, TRM demonstrates remarkable progress on the Abstraction and Reasoning Corpus (ARC-AGI), a benchmark used to assess general intelligence in AI. With its minimal 7M parameters, TRM achieves a 44.6% accuracy on ARC-AGI-1 and a 7.8% accuracy on ARC-AGI-2. These results surpass both HRM (which utilized a 27M parameter model) and many of the largest LLMs currently in existence. To put this in perspective, Gemini 2.5 Pro scores only 4.9% on ARC-AGI-2.

Efficiency gains extend to the training process. An adaptive mechanism, ACT – which determines when the model has sufficiently improved an answer and can proceed to a new data sample – has been simplified, thus reducing the need for multiple steps during each training process. This change was made with no major difference in final generalization.

This research from Samsung provides a powerful counterpoint to the prevailing trend of ever-expanding AI model sizes. It demonstrates that carefully designed architectures, capable of iterative reasoning and self-correction, can overcome immensely difficult problems with a fraction of the computational resources. This begs the question: is the future of AI smaller, smarter, and more efficient?