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ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation |
Soundness: 2: fair
Presentation: 2: fair
Contribution: 3: good
Rating: 4: marginally below the acceptance threshold
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
This paper proposes ConciseHint, a framework that improves reasoning efficiency in large reasoning models by injecting concise hints during generation rather than before reasoning or through fine-tuning. The method continuously introduces either manually designed or learned hints to encourage concise thinking while maintaining accuracy. It adaptively controls the hint intensity and injection position based on query complexity through parameters α and β. The extended version, ConciseHint-T, learns hint embeddings from concise reasoning data and introduces a controllable interpolation parameter γ. Experiments on GSM8K, AIME24, and GPQA-Diamond using Qwen and DeepSeek-R1 show token reduction with minimal accuracy loss.
- The proposed training-free ConciseHint framework introduces an interesting perspective by applying in-reasoning intervention rather than pre-reasoning prompting or fine-tuning.
- The paper addresses an important and timely question about improving reasoning efficiency in large reasoning models.
- The experimental results are promising, showing substantial token reduction while maintaining accuracy across the Qwen family of models.
- The idea of adaptive hint injection and its potential for plug-and-play integration with existing efficiency techniques is conceptually appealing.
1. Hyperparameter Selection and Clarity
- The strategy for determining hint injection intensity (parameters α and β) appears ad hoc.
- Although the paper claims that hints are “learnable,” the positions and frequencies of injection are determined through manually tuned hyperparameters rather than learned mechanisms.
- The description of α and β is confusing and inconsistent. For example, the paper suggests α should be “small,” yet sets α = 128 without clarifying what range is considered small or providing sufficient justification through sensitivity analysis
2. Overreliance on Hyperparameters
- The effectiveness of ConciseHint heavily depends on α, β, and γ, which undermines its claim of adaptivity.
- The framework’s practicality is limited without a clear or automated method for selecting these hyperparameters. This raises concerns about reproducibility and robustness across unseen models. Obviously, the current hyperparameter settings are good for Qwen family models, but not as significantly effective in DeepSeek models.
3. Incomplete Experimental Coverage
- The comparison with baselines is uneven across models. For instance, Qwen3-4B includes BeConcise and other baselines, but DeepSeek-R1-14B results omit some of these.
- It is unclear why BeConcise or similar prompting-based baselines cannot be combined with ConciseHint for stronger comparisons. These inconsistencies suggest the experiments are not yet fully comprehensive or standardized.
4. Overclaim on Reasoning
- The evaluation focuses primarily on math and physics QA datasets (GSM8K, AIME24, GPQA-Diamond). Such domains do not represent general reasoning; broader evaluations on coding, commonsense, or multimodal reasoning datasets are needed.
- As a result, the paper’s claim of improving “reasoning efficiency” in general is somewhat overstated.
5. Trade-off Between Accuracy and Efficiency
- In Table 2, performance noticeably degrades when γ increases (e.g., γ = 1), indicating potential instability or limited generalization.
- The results reveal a clear trade-off between conciseness and accuracy, which should be analyzed more thoroughly rather than only reported. A discussion of this trade-off would help readers better understand practical deployment choices.
6. Writing and Presentation Issues
- The paper is difficult to read, with dense notation and numerous hyperparameters that could be summarized more clearly.
- Several equations (e.g., Eq. (1)–(3)) could benefit from intuitive explanations or visual aids describing the adaptive behavior.
Please refer to the weaknesses. |
Moderately AI-edited |
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ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation |
Soundness: 3: good
Presentation: 2: fair
Contribution: 2: fair
Rating: 4: marginally below the acceptance threshold
Confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked. |
The authors propose ConciseHint to tackle the inefficiency of reasoning models. This is a framework that injects "concise hints" (like "make answer concise!") during the reasoning process, rather than only prompting before it. The method’s key features include: (1) Complexity-Adaptive Intensity, which automatically adjusts how often it injects hints. (2) Dynamic Injection Position, which dynamically adjusts where it injects the hint in the text. A trained version, ConciseHint-T, learns hint embeddings from data to further improve efficiency. Experiments show ConciseHint significantly reduces token usage (e.g., ~49% on GSM8K) while maintaining accuracy.
- 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 "accuracy" from a single run or small average is not statistically sound, especially when using sampling (temp 0.6).
- The trained version, ConciseHint-T, is trained only on the GSM8K (math) dataset. The paper's claim that it "generalizes well" to completely different domains like GPQA has limited evidence.The hyperparameters are sensitive and need careful choice.
- The paper claims $\alpha$ and $\beta$ work well when fixed, but the appendix shows that poor choices can "significantly undermine accuracy."
[1] AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
[2] Adaptthink: Reasoning models can learn when to think
[3] O1-pruner: Length-harmonizing fine-tuning for o1-like reasoning pruning
[4] Autol2s: Auto long-short reasoning for efficient large language models
- Could fine-tuning the model (even with a lightweight method like LoRA) on the same concise dataset also work?
- What is the end-to-end latency impact of the periodic KV-cache invalidation?
- Are there any other proxies for complexity besides current length? |
Fully human-written |
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ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation |
Soundness: 2: fair
Presentation: 3: good
Contribution: 2: fair
Rating: 4: marginally below the acceptance threshold
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
This paper focuses on the reasoning efficiency problem of large reasoning models (LRMs) with CoT that tend to generate verbose intermediate reasoning steps. To improve the efficiency, the paper proposes ConciseHint, a method that performs intervention during the generation of reasoning, making the reasoning process concise. ConciseHint continuously injects hints (either a designed text or continuous embeddings) to control the subsequent token generation. ConciseHint also adaptively adjust the injection intensity according to the complexity of the query, which balances efficiency-accuracy by applying a lower hint intensity for complex queries and a higher intensity for easy ones.
- 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 the reasoning steps. It’s unclear how the injected hints change the reasoning path, whether they improve clarity, oversimplify the reasoning, or disrupt its flow. A more detailed quality or behavioral analysis would make the work stronger.
- Limited novelty. Although the method is simple and practical, its scientific novelty is modest. The hint scheduling and injection mechanisms are relatively intuitive and heuristic, and the paper does not analyze why or how the hints affect the model’s reasoning process. Exploring how sensitive the model is to different hint designs or injection positions would add important insights.
The paper does not discuss the generalizability of the designed hint. Would the same hint work effectively across other reasoning domains, or would they require task-specific tuning? |
Moderately AI-edited |
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ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation |
Soundness: 3: good
Presentation: 2: fair
Contribution: 2: fair
Rating: 6: marginally above the acceptance threshold
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
To address the inefficiency of Large Reasoning Models (LRMs) caused by verbose Chain-of-Thought (CoT) generation (e.g., redundant tokens and self-checks), this paper proposes ConciseHint, an in-reasoning intervention framework. Unlike existing methods that only intervene before reasoning (e.g., input prompting, SFT/RL), ConciseHint continuously injects learnable hints (manually designed text or data-learned embeddings) during the reasoning generation process. It adaptively adjusts hint intensity based on reasoning length to avoid over-intervention on complex queries, and dynamically shifts injection positions (from head to tail, capped at \(\tau_{k} \cdot 0.8\)) to balance accuracy and computational cost. An enhanced variant, ConciseHint-T, further optimizes hints via supervised fine-tuning on concise data, enabling controllable reasoning length through embedding interpolation. Experiments on GSM8K, AIME24, and GPQA-Diamond with models like Qwen3 series and DeepSeek-R1 show that ConciseHint reduces tokens by 27%-65% while maintaining accuracy, and seamlessly integrates with existing methods (e.g., BeConcise, Deer) to further boost efficiency.
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 and data-learned hints (ConciseHint-T), with interpolation-based controllability to balance token reduction and performance.
Strong Compatibility: Serves as a plug-and-play plugin that integrates seamlessly with existing efficient methods, pushing the upper bound of reasoning efficiency without modifying the base model.
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 provides no breakdown of which specific redundancies are eliminated (e.g., transition words like "wait", repetitive premise restatements, logically superfluous steps). Without this analysis, readers cannot confirm if the framework targets meaningful redundancy (vs. accidental suppression of critical logic).Unvalidated Adaptive Parameters: The default values for \(\alpha=128\) (base interval) and \(\beta=0.2\) (adaptive coefficient) are provided without parameter sensitivity analysis. It is unknown how these values perform across tasks (e.g., AIME24’s long reasoning vs. GSM8K’s short steps) or if optimal parameters exist for different scenarios—hindering practical adoption.Hint Content Impact Ignored: The paper tests only one manual hint ("make answer concise!") and a single training dataset (MixChain-Z-GSM8K) for ConciseHint-T.
Lack of Error Analysis and Edge Case RobustnessWeakness Details:Unanalyzed Accuracy Drops: The paper notes minor accuracy drops (e.g., Qwen3-1.7B on GSM8K: 90.87% → 88.01% for \(\gamma=1.0\)) but provides no analysis of why these drops occur.
see Weaknesses |
Heavily AI-edited |