Samsung’s Tiny 7M-Parameter AI Beats Giants in Reasoning

Samsung AI, TRM, Tiny Recursive Model, DeepSeek-R1, Gemini 2.5 Pro, AI Reasoning, AI Efficiency, Machine Learning, Neural Networks, Alexia Jolicoeur-Martineau

In a ground breaking leap for artificial intelligence, Samsung AI researchers have unveiled a model that challenges one of the industry’s most entrenched beliefs — that bigger models are better. Their new Tiny Recursive Model (TRM), containing just 7 million parameters, has outperformed massive language models thousands of times larger on complex reasoning benchmarks, proving that intelligent architecture can triumph over brute computational power.

A Paradigm Shift in AI Thinking

The research, led by Alexia Jolicoeur-Martineau at Samsung SAIL Montreal, is detailed in a paper titled “Less is More: Recursive Reasoning with Tiny Networks.” Unlike the industry trend of building enormous models with hundreds of billions of parameters — often costing millions of dollars in training — Samsung’s approach shows that smart design and recursive thinking can deliver superior reasoning performance with a fraction of the resources.

This discovery could redefine how the world thinks about artificial intelligence development. Instead of scaling endlessly, researchers might now look toward recursive reasoning as the next frontier of AI progress.

Stunning Performance Across Reasoning Benchmarks

The Tiny Recursive Model (TRM) has delivered results that have stunned the research community, setting new records in reasoning-based AI tasks:

These results are particularly impressive because TRM uses less than 0.01% of the computational resources compared to its competitors. While other models rely on sheer size, Samsung’s TRM achieves its intelligence through recursive refinement — a mechanism inspired by human problem-solving patterns.

The Secret: Recursive Reasoning Like the Human Brain

At the heart of TRM’s success lies its recursive reasoning approach. Unlike traditional large language models that generate an answer in a single pass, TRM operates in an iterative loop — continuously refining its reasoning and solution over multiple steps.

Here’s how it works:

1. The model generates an initial answer.
2. It uses an internal “scratchpad” to critique its reasoning.
3. It updates and refines the solution — up to 16 recursive cycles.
4. The final output is significantly more accurate and logically consistent.

This design mimics human cognitive behavior, where reasoning is not a one-shot process but a continuous refinement of ideas. It also addresses one of AI’s biggest weaknesses — the tendency for early mistakes to snowball throughout the reasoning process.

As lead researcher Jolicoeur-Martineau wrote on social media:

“The notion that one must depend on extensive foundational models trained for millions of dollars by major corporations to tackle difficult tasks is misleading. Recursive thinking, not scale, may be the true key to solving abstract reasoning challenges.”

A Simpler Yet Smarter Architecture

TRM also represents a dramatic simplification compared to its predecessor, the Hierarchical Reasoning Model (HRM).
While HRM required two neural networks and complex mathematical formulations, TRM uses just a single two-layer network — yet achieves superior results. This simplicity not only improves efficiency but also makes the model easier to train, deploy, and interpret.

Its minimalism, combined with recursion, allows TRM to punch far above its weight — achieving results previously thought impossible for models of such size.

Why This Breakthrough Matters

Samsung’s TRM could have far-reaching implications across the AI landscape:

• Lightweight AI for Edge Devices: TRM’s small size makes it ideal for smartphones, IoT devices, and embedded systems.
• Faster and Cheaper Training: Lower compute requirements mean faster experimentation and reduced costs.
• New Path for AI Research: Shifts the focus from “bigger is better” to “smarter is better.”
• Human-like Reasoning: Recursive refinement could lead to AI systems that reason and learn more like humans.

The Future: Less Might Truly Be More

While TRM’s current strengths are most evident in structured, logic-based tasks (like puzzles, mazes, and abstract reasoning), the implications are profound. If recursive reasoning can be generalized to broader domains — such as natural language, planning, or creative problem-solving — this could signal the dawn of a new generation of efficient, human-like AI systems.

In a world racing toward ever-larger models, Samsung’s TRM is a bold reminder that intelligence isn’t just about scale — it’s about strategy.

Frequently Asked Questions

1. What is Samsung’s Tiny Recursive Model (TRM)?

Samsung’s Tiny Recursive Model (TRM) is a new artificial intelligence model with just 7 million parameters. Despite its small size, it outperforms much larger models on reasoning benchmarks like ARC-AGI, Sudoku, and maze-solving tasks. It was developed by Alexia Jolicoeur-Martineau and the Samsung SAIL Montreal team.

2. How does TRM outperform large AI models like Gemini or OpenAI’s o3-mini?

TRM uses a recursive reasoning process — instead of generating answers in one pass, it iteratively refines its responses through multiple steps, much like how humans think. This design helps it correct early mistakes and achieve high accuracy even with limited parameters.

3. What benchmarks did Samsung’s TRM perform well on?

TRM achieved 44.6% accuracy on ARC-AGI-1, outperforming models like DeepSeek-R1, Google Gemini 2.5 Pro, and OpenAI o3-mini.
It also scored 7.8% on the more challenging ARC-AGI-2 test, 87.4% on Sudoku-Extreme puzzles, and 85.3% on Maze-Hard tasks.

4. What makes recursive reasoning different from traditional AI?

Traditional AI models generate an output in a single forward pass. Recursive reasoning models like TRM, however, loop through reasoning steps multiple times, refining answers progressively. This makes them more logical, adaptable, and human-like in problem-solving.

5. Why is TRM considered a breakthrough in AI research?

Because it challenges the long-held belief that “bigger is always better” in AI. TRM proves that small models with smarter architecture can outperform massive systems — potentially making AI more efficient, affordable, and accessible for everyone.

6. Can TRM be used in real-world devices?

Yes. TRM’s lightweight design makes it ideal for edge devices such as smartphones, IoT systems, and embedded AI applications. Its low computational requirements open up possibilities for high-level reasoning on low-power hardware.

7. Who led the research behind Samsung’s TRM?

The project was led by Alexia Jolicoeur-Martineau from Samsung SAIL Montreal.
The findings are detailed in the research paper titled “Less is More: Recursive Reasoning with Tiny Networks.”

8. What does TRM mean for the future of AI?

TRM could mark the beginning of a new AI era focused on smarter, smaller, and more efficient systems.
It shows that the future of artificial intelligence may depend less on scale and more on clever architecture and recursive reasoning.