<ul data-eligibleForWebStory="false"><li>Enhancing reasoning capabilities in Large Language Models (LLMs) is a key focus in research.</li><li>A new approach, TeaR, has been proposed to teach LLMs to reason better by leveraging data curation and reinforcement learning.</li><li>TeaR aims to improve general reasoning abilities by guiding models in discovering optimal reasoning paths through code-related tasks.</li><li>Extensive experiments show significant performance improvements with TeaR, achieving a 35.9% improvement on Qwen2.5-7B and 5.9% on R1-Distilled-7B benchmarks.</li></ul>

Teaching LLM to Reason: Reinforcement Learning from Algorithmic Problems without Code

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