TL;DR
ConciseHint is a novel framework that enhances reasoning efficiency by encouraging large reasoning models to produce concise explanations during generation, without sacrificing performance, through adaptive hints injection.
Contribution
It introduces a new method for directly promoting concise reasoning during generation, improving efficiency and flexibility of large reasoning models.
Findings
Effective in producing concise reasoning explanations
Maintains performance while improving efficiency
Compatible with existing reasoning methods
Abstract
Recent advancements in large reasoning models (LRMs) like DeepSeek-R1 and OpenAI o1 series have achieved notable performance enhancements on complex reasoning tasks by scaling up the generation length by Chain-of-Thought (CoT). However, a critical issue is their tendency to produce excessively verbose reasoning processes, leading to the inefficiency problem. Existing literature on improving efficiency mainly adheres to the before-reasoning paradigms such as prompting and reasoning or fine-tuning and reasoning, but ignores the promising direction of directly encouraging the model to speak concisely by intervening during the generation of reasoning. In order to fill the blank, we propose a framework dubbed ConciseHint, which continuously encourages the reasoning model to speak concisely by injecting learnable hints (manually designed or learned on concise data) during the generation of…
Peer Reviews
Decision·Submitted to ICLR 2026
- The approach of inserting a short, instructive hint (e.g., “make answer concise”) into the model’s reasoning process is simple and straightforward. - The strategy for adjusting the hint injection intervals and positions is intuitive and well-motivated. - The paper is clearly written and logically structured.
- Limited evaluation. The experiments are run on only three datasets, and two of them (AIME24 and GPQA-Diamond) are quite small. It would be helpful to test the method on more datasets from different domains. - Performance drop. While ConciseHint successfully reduces the number of generated tokens, it also causes a clear drop in accuracy, especially for ConciseHint-T. - Narrow analysis. The evaluation mainly looks at accuracy and token count. It would be valuable to also assess the quality of t
- The "in-reasoning intervention" paradigm is new and interesting; it is intelligently designed to avoid hurting performance. - The method is flexible and can be integrated with other existing efficiency methods, and it can also be controlled either in a training-free or a trained manner. - Experimental results show that the method works effectively across multiple state-of-the-art models (Qwen3 series, DeepSeek-R1) and challenging benchmarks.
- The core assumption relies on the idea that the current reasoning length is a good proxy for query complexity. This largely depends on specific models, as a model can be verbose on an easy problem or concise on a hard one. - The evaluation methodology is weak: - The paper is missing comparisons to other efficient reasoning methods like AlphaOne, AdaptThink, O1-pruner and Autol2s. - Missing multiple runs and pass@1: For small, complex benchmarks like AIME24 (only 30 problems), reporting "ac
Novel In-Reasoning Intervention Paradigm: Breaks the limitation of "pre-reasoning intervention" in existing works, directly guiding conciseness during token generation—opening a new direction for efficient LRMs. Adaptive and Dynamic Mechanisms: Designs complexity-aware hint intensity (adapting to query difficulty via reasoning length) and dynamic injection positions, ensuring accuracy while maximizing efficiency. Flexible and Controllable Hint Design: Supports both training-free manual hints a
The largest model tested is 14B (DeepSeek-R1-14B)—no validation on ultra-large LRMs (70B+, e.g., Qwen3-72B, GPT-4o) where CoT verbosity and computational costs are more severe. Larger models often have more stable reasoning chains; it is unclear if ConciseHint’s intervention is redundant or still effective here. Lack of Redundancy Targeting and Parameter SensitivityWeakness Details:Unquantified Redundancy Suppression: The paper claims ConciseHint reduces "redundant tokens and self-checks" but p
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