arXiv:2510.25741v5 Announce Type: replace Abstract: Modern LLMs are trained to "think" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training data. We present and open-source Ouro, named after the recursive Ouroboros, a family of pre-trained Looped Language Models (LoopLM) that instead build reasoning into the pre-training phase through (i) iterative computation in latent space, (ii) an entropy-regularized objective for learned depth allocation, and (iii) scaling to 7.7T tokens. Ouro 1.4B and 2.6B models
The continuous push for more efficient and capable AI models is driving innovation in training methodologies and architectures, with researchers seeking to integrate reasoning more deeply into the pre-training phase.
This research suggests a potential paradigm shift in how large language models are trained, moving from post-training reasoning (like CoT) to architecture-native reasoning capabilities, which could lead to significantly more powerful and efficient AI systems.
AI models may evolve to inherently 'think' during pre-training rather than learning explicit reasoning processes afterward, potentially reducing computational overhead and enhancing capabilities.
- · AI research institutions
- · Cloud computing providers
- · Developers leveraging advanced LLMs
- · Companies relying solely on CoT for reasoning
- · Legacy LLM architectures
The new LoopLM architecture could lead to AI models with improved reasoning and problem-solving abilities.
Enhanced AI capabilities could accelerate automation across various industries and contribute to the development of more autonomous AI agents.
A fundamental shift in AI reasoning could enable breakthroughs in scientific discovery and complex system management, requiring new paradigms for human-AI interaction.
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Read at arXiv cs.CL