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arXiv Machine Learning By Ayhan Suleymanzade, Chanhyuk Lee, Floor Eijkelboom, Nicholas M. Boffi, \.Ismail \.Ilkan Ceylan, Jinwoo Kim

Thinking with Looped Flows

Read the original on arXiv Machine Learning →

The paper introduces looped flows, a new approach that trains looped models using local denoising objectives to overcome the difficulty of training early updates for future ones. By enforcing temporal association through progressively decreasing noise levels and shared noise, the method encourages recurrent states to transfer useful computation over time. Inference is framed as integrating the velocity of a probability flow parameterized by the learned denoiser, allowing the model to solve harder problems by allocating more computation and producing multiple valid predictions from different initial noise samples. Across six reasoning benchmarks, looped flows outperform prior state‑of‑the‑art looped models, achieving 58.8% accuracy on ARC‑AGI‑1 and 12.2% on ARC‑AGI‑2.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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