<ul><li>Large language models (LLMs) excel at complex tasks due to their improved reasoning abilities but often overlook the trade-off between reasoning effectiveness and computational efficiency.</li><li>To address this issue, a new framework called Learning to Think (L2T) has been proposed, which is an information-theoretic reinforcement fine-tuning approach for LLMs.</li><li>L2T treats each query-response interaction as a hierarchical session of multiple episodes and uses a universal dense process reward to optimize reasoning with fewer tokens, without the need for additional annotations.</li><li>Theoretical analyses and empirical results demonstrate that L2T optimizes the model through reinforcement learning, leading to improved reasoning effectiveness and efficiency across various tasks and models.</li></ul>

Learning to Think: Information-Theoretic Reinforcement Fine-Tuning for LLMs

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