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ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation

Created by
  • Haebom

Author

Siao Tang, Xinyin Ma, Gongfan Fang, Xinchao Wang

Outline

In this paper, we propose a ConciseHint framework to induce concise representations during the inference process generation to address the inefficiency problem caused by excessively detailed inference process of large-scale inference models (LRMs). ConciseHint continuously injects text-based hints, which are manually designed or learned with concise data, into the token generation process to induce the model to infer concisely. It also adjusts the hint strength according to the complexity of the query to prevent model performance degradation. Experimental results on state-of-the-art LRMs such as DeepSeek-R1 and Qwen-3 series demonstrate that ConciseHint can effectively reduce the length of the inference process without any performance degradation. For example, using the Qwen-3 4B model, we achieve a 65% reduction in inference length on the GSM8K benchmark while maintaining almost the same accuracy.

Takeaways, Limitations

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Takeaways:
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A novel solution to the inefficient over-inference problem of LRM is presented.
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Proposing an effective method to induce conciseness during inference process generation (ConciseHint).
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Implement a mechanism to adaptively adjust hint strength based on query complexity.
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We present experimental results showing that the inference length is significantly reduced without performance degradation in state-of-the-art LRMs.
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Limitations:
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Lack of detailed description of the design and learning process of the hints.
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Further experiments with different types of LRMs and benchmarks are needed.
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Further research is needed on the generalization performance and scalability of ConciseHint.
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Subjectivity of manually designed hints and lack of discussion on __T11457_____.
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