New Method DeepConf Boosts Math Reasoning in Language Models
A new method, DeepConf, has been developed to enhance mathematical reasoning in language models without extra training. Led by Fu et al., this approach promises improved efficiency and accuracy.
DeepConf works in two modes. In offline mode, it achieved 99.9% accuracy on AIME 2025 tasks using gpt-oss-120B model. In online mode, it maintained 97.9% accuracy while reducing token consumption by up to 84.7% compared to standard majority voting. This efficiency is crucial as energy costs rise, questioning the long-term viability of 'thinking' models.
The method filters low-quality reasoning traces using internal confidence signals, improving both efficiency and accuracy. An early-exit scheme truncates overthinking without compromising results. However, it may struggle when a model is overly confident in incorrect answers, making the conservative variant a safer choice.
DeepConf's potential is significant. It could play a central role in language model development due to its efficiency and comparable or better results. By reducing computational costs, it addresses the economic viability concerns of 'thinking' models. With further refinement, DeepConf could help make mathematical reasoning in language models more practical and accessible.
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