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Jiaxuan Gao, Shu Yan, Qixin Tan, Lu Yang, Shusheng Xu, Wei Fu, Zhiyu Mei, Kaifeng Lyu, Yi Wu
Outline
This paper proposes the concept of reasoning efficiency frontiers to address the inefficiency problems caused by excessive detail and redundancy in the chain-of-threading (CoT) inference process of large-scale reasoning models (LRMs). Based on empirical upper bounds obtained through various fine-tuning methods and learning configurations, we propose the reasoning efficiency gap (REG) metric, which quantifies how far a fine-tuned LRM deviates from this bound. Mathematical benchmark evaluations reveal a significant efficiency gap among existing methods. To reduce this gap, we propose REO-RL, a reinforcement learning algorithm that minimizes REG by targeting a sparse set of token budgets. REO-RL leverages numerical integration over strategically selected budgets to approximate the overall efficiency goal with a small token budget. Experimental results show that REO-RL reduces REG by more than 50% for all evaluated LRMs.
Takeaways, Limitations
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Takeaways:
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We propose REG, a unified metric for evaluating the inference efficiency of LRM, and through this, we clearly reveal the limitations of existing methods.
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We propose REO-RL, a reinforcement learning algorithm that minimizes REG, and verify its effectiveness through experiments.
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Suggesting new research directions for improving the inference efficiency of LRM.
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We demonstrate the usefulness of the REG metric in effectively capturing the trade-off between efficiency and accuracy.
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Limitations:
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Fine-tuning the LRM to perfectly fit the efficiency boundary remains an unresolved challenge.
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Further research is needed to evaluate the generalization performance of the proposed REO-RL algorithm and its applicability to various problem types.
