The Return of Recursion: How 5M-Parameter Models Are Outperforming Frontier LLMs on Reasoning in 2026
In 2026, recursive models with 5-7 million parameters are outperforming larger frontier LLMs on reasoning tasks. These models achieve significant speed and efficiency improvements by reasoning in latent space rather than generating tokens. The revival of recursion in AI is attributed to modern training methods that address previous issues with recurrent neural networks.
- ▪Tiny recursive models are achieving state-of-the-art results on deterministic reasoning tasks.
- ▪Probabilistic TRM models use Gaussian noise to enhance performance on complex puzzles.
- ▪Recursive architectures are making a comeback due to their efficiency and reduced computational costs.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1140118) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ramsis Hammadi Posted on May 22 The Return of Recursion: How 5M-Parameter Models Are Outperforming Frontier LLMs on Reasoning in 2026 #ai #recursive #opensource #news The Return of Recursion: How 5M-Parameter Models Are Outperforming Frontier LLMs on Reasoning in 2026 TL;DR Summary Tiny recursive models with 5-7 million parameters are achieving state-of-the-art on deterministic reasoning tasks that frontier LLMs score 0% on — including Sudoku-Extreme, ARC-AGI puzzles, and maze…
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