ByteDance's Looped LMs: Small Models Match 12B via Latent Reasoning
burny_tech · x · 2026-07-08
ByteDance, collaborating with Yoshua Bengio and others, released 'Scaling Latent Reasoning via Looped Language Models.' They propose the Ouro series of looped language models (1.4B/2.6B), claiming that through latent loop reasoning, they can match the performance of SOTA LLMs up to 12B parameters across multiple benchmarks. The authors acknowledge external skepticism regarding these scaling conclusions. This paper is a representative work for observing how looped/latent reasoning LLMs perform at scale.
Related event: ByteDance and Bengio Propose Looped Language Models for Latent Reasoning(3 posts)→
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