Single Task Algorithmic Reasoning Models: How a Small Model Outperforms a Large LLM
The article discusses the need for reasoning models to go beyond text-based reasoning, incorporating tools, multimodal modes, and latent representations. The authors introduce STARM, a framework for training recursive reasoning models that significantly outperform previous open-source counterparts.
The authors argue that by 2026, excellent reasoning models will require more than just textual reasoning. High-quality answers to most user questions will necessitate tool use, multimodal reasoning modes, and sometimes exotic methods like reasoning in latent network representations. They present the STARM framework for training recursive reasoning models, which substantially surpass previous open-source models in quality, as demonstrated by both colleagues' results and their own research.
Source: Habr — хаб ИИ —
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