Title: Let LRMs Break Free from Overthinking via Self-Braking Tuning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Large Reasoning Models

Efficient Reasoning
Abstract: Large reasoning models (LRMs), such as OpenAI o1 and DeepSeek-R1, have significantly enhanced their reasoning capabilities by generating longer chains of thought, demonstrating outstanding performance across a variety of tasks. However, this performance gain comes at the cost of a substantial increase in redundant reasoning during the generation process, leading to high computational overhead and exacerbating the issue of overthinking.
Although numerous existing approaches aim to address the problem of overthinking, they often rely on external interventions.
In this paper, we propose a novel framework, **Self-Braking Tuning**(SBT), which tackles overthinking from the perspective of allowing the model to regulate its own reasoning process, thus eliminating the reliance on external control mechanisms. We construct a set of overthinking identification metrics based on standard answers and design a systematic method to detect redundant reasoning. This method accurately identifies unnecessary steps within the reasoning trajectory and generates training signals for learning self-regulation behaviors. Building on this foundation, we develop a complete strategy for constructing data with adaptive reasoning lengths and introduce an innovative braking prompt mechanism that enables the model to naturally learn when to terminate reasoning at an appropriate point.
Experiments across mathematical benchmarks (AIME, AMC, MATH500, GSM8K) demonstrate that our method reduces token consumption by up to 60\% while maintaining comparable accuracy to unconstrained models.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to study and address the issue of overthinking in current large reasoning models (LRMs). The authors first introduce two metrics — Reasoning Efficiency Ratio and Overthinking Marker Ratio — to quantitatively measure the extent of overthinking in models. Based on these metrics, they propose an adaptive data processing pipeline to construct Self-Braking Tuning datasets, which are designed to teach the model to stop reasoning at the appropriate time. The goal is to improve the model’s inference efficiency after training on the constructed datasets, without causing overthinking. Experimental results show that models trained on the Self-Braking Tuning datasets cost fewer tokens than the baseline model trained on the original o1-like datasets does.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n\n(s1) The paper can be easily followed, and Figure 2 well describes the entire pipeline.\n\n(s2) Addressing overthinking issue is a widely studied problem.\n\n**Weaknesses**: \n\n(w1) First, regarding the two metrics proposed to measure the overthinking issue: Reasoning Efficiency Ratio has already been introduced in prior work [1], and Overthinking Marker Ratio is not a very reliable metric (as acknowledge by the authors). Therefore, this part of the contribution is weak. Moreover, the authors do not clearly clarify how the reasoning steps are segmented for calculating the Reasoning Efficiency Ratio. \n\n(w2) Then, the Overthinking Score is also quite heuristic. The choice of $\\beta$ = 0.1 also lacks sufficient and convincing justification, and there is no accompanying ablation study to support this design decision.\n\n(w3) Regarding the data construction procedure, it seems that Self-Braking Tuning Exact appears to rigidly retain exactly two full rounds of reasoning in each reasoning path, which seems overly simplistic. Meanwhile, Self-Braking Tuning Dynamic also relies on manually predefined thresholds to determine truncation points. They do not fully align with the authors’ original motivation: enabling the model itself  to autonomously decide when to stop reasoning.\n\n(w4) Regarding the empirical results, though the proposed method can effectively reduce reasoning tokens, the test-time scaling performance is worse than the baseline in most settings in Table 1. Maintaining or improving model performance should take precedence over merely reducing token usage.\n\n(w5) I do not clearly understand the purpose of adding Natural Language Guidance at the reasoning stop points. Moreover, Table 5 shows that adding Natural Language Guidance and using Special Tokens yield similar performance.\n\n(w6) The ablation or convincing explanation on Masked Redundant Thinking  practice is missing, which makes it difficult to assess its actual contribution and necessity.\n\n[1] Chen, Xingyu, et al. \"Do not think that much for 2+ 3=? on the overthinking of o1-like llms.\" ICML 2025\n\nQUESTIONS:\nPlease refer to the weakness part above.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the “overthinking” problem in current large reasoning models (LRMs) by proposing a novel framework called Self-Braking Tuning (SBT). The authors introduce several metrics to detect redundant reasoning and leverage these to construct training data with adaptive reasoning lengths, incorporating a braking prompt mechanism. Models fine-tuned using this approach demonstrate significant token cost reduction, highlighting the effectiveness of the proposed framework.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1.\tThe motivation is clear and well-grounded. Constructing data with a braking prompt mechanism presents a reasonable strategy for addressing the overthinking issue.\n2.\tThe results are strong, achieving nearly 50% reduction in token usage while maintaining performance.\n3.\tThe authors contribute a valuable dataset that can serve as a foundation for future research in this area.\n\n**Weaknesses**\n\n1.\tThe paper is somewhat difficult to follow. One major reason is the introduction of numerous new terms—such as Foundation Solution, Evolution Solution, Reasoning Efficiency Dominance, and Linguistic Indicator Robustness. While I understand the authors’ intention to clarify concepts, these terms could be streamlined or explained in a more accessible way to enhance readability.\n2.\tThe evaluation scope is narrow, focusing exclusively on math-related tasks. Overthinking is not unique to the math domain, so it would be beneficial to include broader benchmarks such as MMLU to demonstrate the framework’s general applicability.\n3.\tThe generalizability of the proposed framework is also a concern. It involves multiple steps and relies on several super-hyperparameters (e.g., thresholds), with no clear guidance on selecting optimal values. Furthermore, the improvements reported in Tables 2–5 appear marginal, raising questions about the robustness and scalability of the method.\n\nQUESTIONS:\n1. Is this a typo? It seems that the model with the lowest token cost shown in Table 1 — Llama-3.1-8B-Instruct — is marked incorrectly.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the problem of \"overthinking\" in Large Reasoning Models (LRMs), where models generate excessively long and redundant chains of thought. The authors propose a novel framework called Self-Braking Tuning (SBT), which teaches LLMs to autonomously regulate their own reasoning process and terminate it at an appropriate point. The core idea is to train the model to develop an \"internal braking mechanism\" rather than relying on external interventions.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n1. The paper introduces an innovative framework to address the problem of LRM “overthinking.” It comprises three components:\n\n   * A data‑driven method for identifying overthinking instances.\n   * An automated, adaptive data‑construction strategy (SBT‑E and SBT‑D).\n   * A unique training mechanism that employs redundancy‑content loss masking and natural‑language braking prompts.\n     This end‑to‑end process, designed to teach the model self‑regulation, represents a novel contribution to the field.\n\n2. The authors provide a clear analytical breakdown of their approach. In the analysis section, they further elucidate the internal dynamics of overthinking and present fine‑grained mitigation strategies, offering valuable insights and potential directions for future work.\n\n3. The paper is very well written and logically coherent. Terminology is precise, and each component’s motivation within the framework is thoroughly explained.\n\n**Weaknesses:**\n\n1. **Performance on Complex Tasks:** The authors themselves note, in the Limitations section, a potential trade‑off when tackling more complex problems. Experimental results also indicate that, as dataset difficulty increases, performance degrades—suggesting that the framework may, to some extent, impair the model’s self‑correction capabilities, which are a key benefit of longer reasoning chains.\n\n2. **Lack of Module‑Level Ablation Studies:** For example, although the “Natural Language Guidance” module is compared against a special‑token baseline, there is no ablation study omitting this module entirely. This omission makes it difficult to quantify each module’s individual contribution.\n\n3. **Hyperparameter Sensitivity and Usability Concerns:** The framework relies on multiple hyperparameters, raising questions about its scalability and ease of use. The threshold ablation experiments show that optimal values vary across datasets—and in some cases produce trade‑offs between accuracy and token usage—indicating that extensive tuning may be required to achieve peak performance.\n\nQUESTIONS:\n1. The authors evaluate their framework exclusively on lightweight models that lack inherent long CoT capabilities. It would be informative to assess its performance on models such as DeepSeek Distill, or others that natively support long CoT reasoning.\n\n2. I recommend that the authors include, in the appendix, both illustrative examples and summary statistics demonstrating the differences between the datasets constructed by their strategy and the original datasets. This would give readers a clearer understanding of the framework’s data‑construction impact.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Self-Braking Tuning (SBT), a novel framework designed to address \"overthinking\" in Large Reasoning Models (LRMs). SBT enables LRMs to identify and terminate unnecessary reasoning autonomously, without external controls. The authors quantify overthinking with a reasoning-efficiency ratio and a marker ratio built from hesitation keywords; these are combined into an Overthink Score to flag superfluous reasoning steps. Then, the paper proposes two data construction strategies, STB-E and STB-D, to train models to \"self-brake\". During supervised fine-tuning, the masked tail receives no gradient, while a short natural-language braking prompt is inserted where the model should halt.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper moves control inside the model instead of relying on external policies, providing a cleaner paradigm for deployment.\n2. The paper proposes a clear and quantitative method for identifying overthinking. The \"Overthink Score,\" combining structural efficiency and linguistic markers, provides a robust and interpretable way to detect redundancy.\n3. The authors test their framework across multiple models of varying sizes and types, as well as on a diverse set of mathematical benchmarks, demonstrating the broad applicability and effectiveness of their approach.\n\nWeaknesses:\n1. SBT assumes <think>…</think> delimiters, where only partial models internally have them during inference. The method may be hard to generalize to most models.\n2. The data construction strategies rely on predefined thresholds for the Overthink Score. These thresholds were determined empirically and may require manual tuning for different models, tasks, or datasets, which could limit the framework's adaptability.\n3. When problems truly require long chains, premature braking may silently omit critical steps and compromise the model's performance.\n4. The Overthink Marker may be changed based on models. It can not be generalized to other models.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nSee weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}