TL;DR
Researchers introduce Ouro, a family of Looped Language Models (LoopLM) that incorporate iterative latent-space reasoning into the pre-training phase. Small Ouro checkpoints (1.4B and 2.6B) trained on 7.7T tokens reportedly match performance of much larger (up to 12B) state-of-the-art models on a range of benchmarks, and produce latent reasoning traces more consistent with final outputs than explicit chain-of-thought.
What happened
A multi-author team published a paper presenting Ouro, a new direction for scaling reasoning in language models by moving reasoning into pre-training. The proposed LoopLM family is trained to perform iterative computation in latent space rather than relying primarily on explicit text-based chain-of-thought at inference time. Training uses an entropy-regularized objective designed to allocate learned computation depth, and the authors trained models on a corpus amounting to 7.7 trillion tokens. Two released checkpoints, identified as Ouro 1.4B and Ouro 2.6B, are reported to reach performance comparable to state-of-the-art models up to 12 billion parameters across diverse benchmarks. Controlled experiments in the paper attribute the gains to improved manipulation of stored knowledge rather than increased knowledge capacity. The team also reports that LoopLM produces internal reasoning traces that align more closely with its final outputs than typical explicit chain-of-thought outputs. The project is open-sourced and the paper is available on arXiv.
Why it matters
- Shifts reasoning from post-training prompts (e.g., chain-of-thought) into the model’s pre-training regime, suggesting a different scaling axis for LLMs.
- Smaller LoopLM checkpoints reportedly match much larger SOTA models, which could influence model-size versus capability trade-offs.
- Improved alignment between internal reasoning traces and final outputs may aid interpretability and debugging of model reasoning.
- Open-source release enables independent verification and community-driven follow-up work.
Key facts
- Paper title: "Scaling Latent Reasoning via Looped Language Models."
- Authors list includes Rui-Jie Zhu, Zixuan Wang, Kai Hua, Tianyu Zhang, Yoshua Bengio, Jason Eshraghian, and many others.
- Submitted to arXiv on 29 Oct 2025 and revised 17 Nov 2025 (arXiv:2510.25741).
- Ouro is a family of "Looped Language Models" (LoopLM) that perform iterative computation in latent space.
- Training incorporates an entropy-regularized objective intended to learn computation depth allocation.
- Models were trained on a dataset totaling 7.7 trillion tokens.
- Two checkpoints, Ouro 1.4B and 2.6B, are reported to match results of state-of-the-art models up to 12B parameters across a range of benchmarks.
- Authors claim the performance advantage derives from superior knowledge manipulation rather than increased knowledge capacity.
- The paper reports that LoopLM’s internal reasoning traces better align with final outputs than explicit chain-of-thought.
- The authors have made the model and related code available (open-source link provided in the paper).
What to watch next
- Independent replication and benchmarking by the research community to confirm reported gains (not confirmed in the source).
- Broader evaluations on diverse tasks including robustness, safety, and real-world applications (not confirmed in the source).
Quick glossary
- latent space: A compressed, internal representation of input data used by models to store and manipulate features during computation.
- chain-of-thought (CoT): An explicit text-based prompting or decoding technique that encourages a model to generate intermediate reasoning steps before producing a final answer.
- entropy-regularized objective: A training loss modification that includes an entropy term to influence distributions learned by the model, often to encourage exploration or control complexity.
- pre-training: The initial phase of training a language model on large corpora of text to learn general representations before any task-specific fine-tuning.
- reasoning trace: A record or representation of intermediate computation steps a model produces while deriving an output, which can be explicit text or internal latent states.
Reader FAQ
What is Ouro?
Ouro is a family of Looped Language Models (LoopLM) that embed iterative latent-space reasoning into pre-training.
How big are the released models?
The released checkpoints reported in the paper are Ouro 1.4B and Ouro 2.6B parameters.
Do these models outperform larger models?
The authors report that Ouro 1.4B and 2.6B match the results of up to 12B state-of-the-art models across a range of benchmarks.
Is the code or model available?
Yes — the authors state the model and related resources are open-sourced and provide a link in the paper.
Does Ouro reduce overall training compute or cost?
not confirmed in the source
Computer Science > Computation and Language [Submitted on 29 Oct 2025 (v1), last revised 17 Nov 2025 (this version, v4)] Scaling Latent Reasoning via Looped Language Models Rui-Jie Zhu, Zixuan…
Sources
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