Large language models (LLMs) process extensive datasets to generate coherent outputs, focusing on refining chain-of-thought (CoT) reasoning. This methodology enables models to break down intricate problems into sequential steps, closely emulating human-like logical reasoning. Generating structured reasoning responses has been a major challenge, often requiring extensive computational resources and large-scale datasets to achieve optimal performance. Recent efforts aim to enhance the efficiency of LLMs, ensuring they require less data while maintaining high reasoning accuracy. One of the primary difficulties in improving LLM reasoning is training them to generate long CoT responses with structured self-reflection, validation, and backtracking. While existing models have demonstrated progress, the training process often demands expensive fine-tuning on extensive datasets. Furthermore, most proprietary models keep their methodologies closed-source, preventing wider accessibility. The need for data-efficient training […]
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