Let LRMs Break Free from Overthinking via Self-Braking Tuning
Authors: Haoran Zhao
Yuchen Yan
Yongliang Shen
Haolei Xu
Wenqi Zhang
Kaitao Song
Jian Shao
Weiming Lu
Jun Xiao
Yueting Zhuang
Venue: NeurIPS 2025 poster ()
Keywords: Large Reasoning Models
Efficient Reasoning
Primary area: deep_learning

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.

Let LRMs Break Free from Overthinking via Self-Braking Tuning
Haoran Z22
Yuchen Y
Yongliang Shen
Haolei Xu
Wenqi Zhang'
Kaitao Song*
Jian Shao!
Weiming Lu!
Jun Xiao
Yueting Zhuang!
Zhejiang University, Tianjin University, Microsoft Research Asia
can1597530tju.ddde edu. [yanyuchen, syl) edu. cn
GitHub: http:://gthhh..coo/////B///////2 Sea-eeelf-BBaaiing Tuning Project:
Abstract
Large reasoning models (LRMs), such as OpenAl ol and DeepSeek-RI, have significantly enhanced their reasoning capabilities by generating longer chains of thought, demonstrating outstanding performance across a variety of tasks. However, at the cost of 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 novel framework, Self- Belf-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 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 terminate reasoning at appropriate point. Experiments across mathematical benchmarks (AIME, AMC, MATH500, GSMSK) demonstrate that our method reduces token consumption by up to 60% while maintaining comparable accuracy to unconstrained models.
this performance gain
Introduction
Large reasoning models (LRMs) such OpenAl's ol (3), Deepseck-R1 [4] QwQ [5), Gemini 2.0 Flash Thinking [6] and Kimi-1.5 [7], excel at mathematical and logical tasks by generating detailed multi-step reasoning, boosting accuracy complex benchmarks {8]. However, this often results in excessively long inference trajectories, frequently consuming thousands of tokens per problem 9 10)
"The first two authors have equal contributions. This work was done when the first author intern Zhejiang University. Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Question: What is the answer of 2 plus ?
DeepSeek-RA Ri-nkttll--iiillll Si1-Ditll-Qwee-77
#Token: 672 #Solution:
<hhinlo let's sew, The user asking for the answer 3. Heres, addition prozlem, Air Airight, understand the question correctly. They
Ined arithmetic: Starting then mone: 3,4, S. So that's 2 is 3, plus another is and one more adding threw two gives five.
of from
0.8
number
6000
0.7
Alternatively using the number line, starting 2 and moving units to the right would land on 5. comitsed>
Token Count
0.5
defintely the methods think confirm that, So the snswer must be
4200
0.5
</hhinbsthe answer
Takenn: 211 #Solution:
2
SBT-Qwen+ -Wen-B
2000
-0.3
<hhinloCkay, so have this math prob om here: 2 plus 3. Alright, let's see, Iremmmber that "plus" means addition, so need to add these two numbers togother, But walt, Let me start by recalingg the basies addition. adding numbers tagether
combining their quantitles. So 2 plus would equals
001
combaning
AIRE24
AIME25
AMC25
M4TH500
GSMEK
Walt, Ill started and then 3 more after that. So that would be plus 3, right? Let me count them. Starting adding 1 would be 3, adding another I would make 4, and then another I would make S, Herm, so is 57 there another way think about it? Maybe using money. Wait, I've gotten the same answer multile times, time end the thinking </thelo2-315,
apples and then added more, would up the apples: [the original)
OpenRt Math
OpanRt-Math- SBTE
OpanR1--auhh
5BT-0
Accinecy
(a)
(b)
Figure 1: Demonstration of Self- Belf-Braking Tuning Effectiveness. In the single-example case (a), the self-braking tuned model exhibits spontaneous termination of overthinking and significantly reduces token usage. On major mathematical benchmarks (b), compared to using OpenRI 1-Math [[] as the SFT dataset, the elf-braking tuned in tokens consumed during inference while maintaining comparable accuracy.
[2 achieves a substantial reduction
leading to increased computational cost, latency, and redundant reasoning that can obscure core solutions [11. This "'overthinkin"" [12] poses significant challenge for practical deployment.
Many recent studies have focused on addressing the problem of overthinking [13 [4] which can be
broadly categorized into three approaches: (1) Model optimization: Apply reinforcement learning (RL) supervised fine-tuning (SFT) to equip models with the ability control reasoning length [IS]- 17]: (2) Reasoning oulpul optimization: Dynamically reducing the number of reasoning steps and output length during inference (0): (3) Adding external restrictions: Imposing external constraints, such as token budgets, to reduce overthinking (2). Most existing methods follow the paradigm of external intervention, relying on complex optimization strategies or introducing additional constraint mechanisms, and have yet to fully explore the intrinsic ability of the model to
mitigate overthinking on its own.
This reliance on external control prompts a fundamental question: Can we enable large reasoning models to autonomously recognize excessive reasoning and terminate their thinking process appropriately? Ideally, a model should intrinsically understand when additional reasoning becomes redundant and halt its thought process without external triggers, similar to how humans naturally conclude their reasoning when reaching sufficient certainty.
To address this challenge, we propose Self-Braking Tuning (SBT), novel framework that teaches LRMs to autonomously identify and terminate redundant reasoning. Unlike previous approaches that impose external constraints, SBT fundamentally reshapes how models perceive and regulate their own reasoning processes. Our key insight is that LRMs can be trained to develop an internal braking mechanism that recognizes when further reasoning becomes unproductive, enabling them to naturally conclude the thought process and transition to formulating the final solution.
Our approach begins with a systematic methodology for identifying overthinking patterns in reasoning trajectorics. By combining metrics such as reasoning efficiency ratio and overthinking label ratio, we precisely pinpoint the transition point at which the model shifts from effective reasoning to redundant computation. Based on this analysis, we develop two complementary data construction strategies: (I) Self-Braking Tuning Exact (SBT-E), which strictly removes redundant reasoning segments based on predefined braking points, allowing the model to enter the conclusion phase earlier; (2) Self- Beff-Braking Tuning Dynamic (SBT-D), which implements step-level monitoring and dynamically halts reasoning when overthinking patterns emerge.
Building upon the high-quality OpenR 1-ath 1 dataset of reasoning trajectories, we constructed two specialized training datasets using these strategies: OpenR OpenRI-Math --athh-SBT-EE and OpenR I-ath- SBT-D. To further enhance the model's 's self-awareness of its reasoning state, we introduce braking

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prompts at the identified braking points, explicitly simulating the recognition of having sufficiently completed the reasoning process. These prompts enable the model to naturally express an awareness of having reached adequate reasoning depth, thus promoting autonomous termination without the need for external signals. Our experimental results demonstrate consistent improvements in reasoning efficiency across mathematical benchmarks of varying difficulty. While maintaining high accuracy, token consumption was reduced by 30% to 60%.
Our contributions can be summarized as follows:
We introduce novel tuning framework that enables LRMs to self-regulate reasoning length without external constraints. Self-Braking Tuning cultivates models' intrinsic ability to recognize and inhibit excessive reasoning, fundamentally improving inference efficiency and response quality.
We propose a systematic methodology for identifying overthinking patterns and develop two complementary data construction strategies: SBT-E and SBT-D, resulting in specialized training datasets for addressing the overthinking problem. These datasets systematically prune redundant reasoning while preserving essential thinking steps.
We demonstrate that models trained with our SBT framework maintain original accuracy levels while reducing token consumption by up to 60% across multiple benchmarks, confirming the effectiveness and generalizability of our approach in enhancing reasoning efficiency.
2
Related Works
Large reasoning models Large reasoning models (LRMs) extend traditional language models with
advanced reasoning capabilities, often enabled by reinforcement learning. OpenAl's ol series [3]
marked key milestone, followed by DeepSeek-RI [4), which matched ol's performance through a
cost-efficient combination of supervised fine-tuning and RL. Subsequent models like Kimi-k1.5 7 and QwQ-32B [S] further solidified the LRM era. Concurrently, alternative approaches have emerged to reduce reliance on RL, leveraging supervised fine-tuning and data distillation. Models such as DeepSeek-Dise -istiil 4 OpenR1 -Math-7B 7B 233. Sky-TI [24]. and LIMO [25] have shown that strong reasoning performance can also be achieved without RL, broadening the design space for LRMs. In addition, recent al algorithmic advances specifically target robustness and scalability of reasoning [26] 28: Math [29] introduces spontaneous step-level self-correction to let models detect and fix erroneous intermediate steps during ohain-of-thought generation, while Infty'Think (30) breaks long- context limits by turning monolithic proof's into iterative short-segment reasoning with concise progress summaries, enabling effectively unbounded reasoning depth with bounded computation.
Efficient reasoning Overthinking is common behavior of large reasoning models, where models generate unnecessarily long answers instead of stopping at the right time. Existing studies addressing overthinking [13] can be grouped into three categories: (1) Model Optimization: This line improves model behavior via post-training techniques, primarily reinforcement learning (RL) and supervised fine-tuning (SFT). RL-based methods design length-sensitive rewards to limit output [16] [7 [5
31.. SFT-based methods use variable-length chain-of-thought (CoT) data and auxiliary constraints to shorten reasoning paths (32 (2) Reasoning Outpul Optimizution: This direction reduces reasoning length at inference time by altering generation strategies. lightthinker [20] compresses
intermediate steps; DEER [19] halts once high confidence is reached; NoThinking [18) skips reasoning
entirely via prompting. These efforts are representative, with many other studies exploring similar
directions 40. (3) These methods impose constraints like token budgets or prompt controls to regulate reasoning behavior. In addition to representative works such as
[1] and
CoD 22 numerous other studies have explored similar constraint-based strategies 41
Methods
In this section, we begin by analyzing the reasoning trajectories of LRMs to understand the patterns
of overthinking (Section 3.1. Based this analysis, we propose metrics to quantify overthinking (Section 3.2) We then introduce our Self-Brakin Braking Tuning framework, which includes two data
construction strategies (Section 3.3 and braking prompt mechanism (Section 3.4
3

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1t Dataset Construction
Stage2: Training
Stage3: Inference
Pipeline Construction
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OpeaRI-Math
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SHT -baant Find beliiin leed raia
The nurbber pasttive nusder distace
Figure 2: Overview of Self Self-Braking Tuning. Left: Data construction process with overthinking identification and self braking truncation strategies, Right: An example of automatic reasoning termination in a trained Self- self-Braking LLM.
3.1 Reasoning trajectory analysis in R1-like models
Understanding the reasoning structure of LRMs is key to addressing overthinking. Analysis of trajectories from models like DeepSeek-RI R1 reveals common pattern: multiple distinct solution attempts are often generated for a single problem. Based on their role and position, these solution segments be grouped into two main types:
1. Foundation solution:
This is the first solution at the beginning of its reasoning process. After comprehending the problem, it proceeds with a step-by-step solution. This forms the foundation of the reasoning process and guides the development of subsequent Evolution Solutions.
2. Evolution solution:
These solutions appear in the later stages of the model's reasoning process and are often introduced with cues such as "Wait," "Alternatively." or "However." Evolution solutions primarily reflect, refine, supplement, or summarize the foundational solution, and may propose new approaches. While this part of the reasoning grants the model self-correction and improvement capabilities, it is also where overthinking most frequently occurs.
To illustrate how LRMs behave across varying difficulty levels, we constructed a set of math evalua- tion benchmarks with graduated difficulty and conducted experiments on DeepSeek-Distill Dinr-eee--Dsilll---e------- The distribution of correct reasoning trajectories is reported in Figure 3 Additionally, a represen- tative example from the MATH500 [44 45) task is presented to demonstrate the concrete forms of Foundation Solution and Evolution Solution.
3.2 Overthinking identification
Identifying overthinking in reasoning trajectories is crucial for designing effective self-braking mechanisms. Based on our structural analysis of reasoning, we propose quantitative framework for detecting and measuring redundancy the OpehRI-Math dataset, which spans a range of mathematical problems and difficulty levels. To distinguish essential reasoning from unnecessary computation, we introduce two complementary metrics capturing different forms of redundancy, integrated through composite scoring mechanism.
Reasoning efficiency ratio Our first metric addresses a fundamental observation in Section 3.1 LRMs frequently derive correct answers relatively early in their reasoning process but continue generating additional solution attempts. To quantify this inefficiency, we introduce the reasoning efficiency ratio, denoted as
FS
n TS
(1)

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Founation Solution vs. Evolution Solutions
How whole-ramber di-tore 196
Poundation Solution: bntiatt Rezsoning
#Tokarc
AIMI
25%
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Solution
Latir wery mase pure dkin mrbi mikals dhoocs memaddy right Counting Paree diiison
MATH500
13%
67
15eluriore6.82
Evolution Solution 2: Second Check
#Tskurc 134
Founation Solation
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Evalution Solutions
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Evolution Solution 4: Final Justifiation
#Toker: 71
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1000 2000
5030
6000
5000
6000
V7tkar
(a)
(b)
In panel (a), we present a representative example from DeepSeek-RI RI--istiill RI-Distill-Qeen-7. In panel (b), we analyze the model's performance across GSM8K, MATH500, and AIME benchmarks, showing that the Foundation Solution plays a critical role across tasks of varying difficulty.
Figure 3:
where FS (first correct steps) represents the number of reasoning steps required to reach the first correct answer within the thinking segment (enclosed by <think> and </think> tags) and TS (total thinking steps) represents the total number steps in the entire thinking segment.
This ratio provides direct measure of reasoning efficiency: values closer to 1 indicate that the model spent most of its reasoning steps before arriving at the correct answer, reflecting focused and efficient reasoning process. In contrast, values closer to 0 suggest that the model continued reasoning extensively after reaching the correct answer, which may indicate overthinking or redundant computation. The step-based calculation enables us assess the structural efficiency of the reasoning process independently of implementation-speeiin token counts.
Overthinking marker ratio While 7) captures structural inefficiency, it does not account for linguistic patterns characteristic of overthinking. Through analysis of high-quality DeepSeek-RI reasoning trajectories, we identified set of linguistic markers strongly associated with overthinking behaviors-term terms that signal reconsideration, verification, or alternative approach exploration. We formalize this observation through the overthinking marker ratio, denoted as Ki::
1 TT
[1, 0 otherwise
if wy E M
K2
Σ
Iw; e M]. I[w, e MM
(2)
where M represents our curated lexicon of overthinking marker terms (complete list in Appendix B TT (Total Tokens) represents the total number of tokens in the thinking segment, 1[:] is the indicator function that equals when w; belongs to M and 0 otherwise.
This ratio quantifies the linguistic footprint of overthinking within reasoning trajectory. Higher values of H, indicate a greater presence of reconsideration and verification language, which typically correlates with redundant reasoning patterns.
Overthink score To develop a comprehensive assessment of overthinking that leverages both structural and linguistic indicators, we introduce the overthink score, a weighted combination of our two metrics:
Overthink Score 3 (1 - B) x (1 (1))
Note that transform n, to (1 (1-7)) n7)) ensure directional consistency, higher values of n, indicate greater efficiency (less overthinking). while higher values K, suggest stronger overthinking patterns. The weighting parameter [0. balances the contribution of each component the final score.
In our implementation, we set , = based on both theoretical considerations and empirical validation. This parameter choice reflects two key insights:
5

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Reasoning efficiency dominance: Early arrival at correct answers significantly reduces compu- tational resources and latency, making (1 m7) the primary component (weighted at 90%). This prioritization aligns with our objective of minimizing unnecessary computation while preserving reasoning quality.
Linguistic indicator robustness: While provides valuable signals about overthinking, it exhibits greater sensitivity to variations in prompt formulation, corpus characteristics, and model-specific output patterns. Assigning lower weight (10%) to mitigates potential noise amplification from these stylistic fluctuations.
3.3 Adaptive inference data construction
Building on our quantitative framework for overthinking identification, we introduce two comple- mentary strategies for constructing adaptive inference length datasets: Self-Braking Tuning Exact (SBT-E) and Self-Braking Tuning Dynamic (SBT-D). Both approaches aim to preserve reasoning depth while cultivating the model's ability to terminate excessive thinking.
Self-Braking Tuning Exact SBT-E implements solution-level truncation strategy with consistent reasoning structure across all training examples. For each trajectory exhibiting overthinking, we preserve the Foundation Solution plus Evolution Solution, followed by a small masked segment from subsequent reasoning. This structured approach ensures the model learns clear boundaries between necessary reasoning and excessive computation.
The Foundation Solution captures the initial structured approach to the problem, while the additional Evolution Solution preserves self-correction capabilities. The masked segment, comprising the beginning of the next Evolution Solution, serves as braking indicator that signals where further reasoning becomes redundant. During training, this masked content does not contribute to the loss function, thereby avoiding reinforcement overthinking patterns. Algorithm in Appendix presents the formal procedure for SBT-E construction.
Self-Brake Braking Tuning Dynamic While SBT-E uses uniform truncation, SBT-D adopts a step-wise adaptive strategy that tailors reasoning length to each problem. It incrementally analyzes each reasoning step to determine individualized termination points.
The process begins with the full preservation of the Foundation Solution. Subsequent steps are added one by one, with the overthink score recalculated after each. Reasoning continues until the score surpasses primary threshold (set to 0.2), allowing complex problems to retain more steps and simpler ones to terminate earlier.
A masking segment is then defined from steps with overthink scores between Ti and secondary threshold 72 (set to 5%). This segment is excluded from loss computation but retained to expose the model to overthinking patterns without reinforcing them. The full procedure is outlined in Algorithm in Appendix.
Together, SBT-E and SBTDD yield the OpenRI-Male -MMa--hTtt and -MatRR--Mah---BBB datasets, each with 92,064 examples, designed to train models to autonomously terminate redundant reasoning.
3.4 Self-regulating braking strategy
Beyond generating adaptive-length reasoning, we introduce complementary training mechanisms enhance the model's 's ability to stop reasoning autonomously, Our Self-Regulating Braking Strategy includes components: masked redundant thinking and natural language braking signals-bold aimed at fostering self-awand awareness of reasoning efficiency.
Masked redundant thinking While both SBT-E and SBT-D identify optimal truncation points, simply cutting off reasoning there doesn't 't help models learn to detect overthinking. Instead, we retain small portion of redundant reasoning and apply loss masking to prevent it from affecting training. For each SBT-E or SBT-D sample, we append this masked segment right after the preserved valid reasoning: in SBT-E, it's the start of the second Evolution Solution; in SBT-D, it includes steps with overthink scores between thresholds 71 and 72. This consistent strategy across both methods ensures balanced exposure.

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By presenting overthinking patterns without calculating their loss, the model learns to distinguish between productive reasoning and redundancy. The masked segment serves as soft boundary cue, encouraging the model to stop reasoning autonomously during inference.
Natural language guidance We further enhance self-regulation by adding clear natural language cues at reasoning stop points. These braking signals are self-reflective statements that show awareness of finishing reasoning. For example, "Wait. I ve gotten the same answer multiple times, time to end the thinking."
Placed at the boundary between preserved and masked content, these cues act as linguistic anchors for stopping decisions. Unlike special tokens external rules, natural language signals fit naturally with the model's abilities, provide explicit metacognitive hints, and keep the reasoning fluent while clearly indicating when to stop.
4 Experiments
We conduct extensive experiments to evaluate the effectiveness of Self- Self-BBrkiig Tuning across various model architectures and mathematical reasoning tasks. Our evaluation aims to answer three key questions: (1) How effectively does SBT reduce token consumption while preserving accuracy? (2) How does performance vary across different model sizes and architectures? (3) How do the two SBT variants (SBT-E and SBT-D) compare in practice?
4.1 Experimental setup
Datasets and training. We curate a dataset of 92K high-quality instances from OpenR 1-Mtth I by applying a 16K context limit and filtering </think> tags). This filtered dataset serves as our baseline. We then construct our SBT-E and SBT-D variants following the methodology detailed in Section 3.3
problematic samples (e.g., those with multiple
We perform supervised fine-tuning on both mathematical specialists (Qwen2.5-Math- 1at---hh---11-BB Instruct (2I) and general-purpose models (Llama-3.2-1) 1B and 1e-ma--.1-8B-Instiie [46]). All models are trained using Megatron-LMM for 3 epochs with a le-5 initial learning rate, cosine decay schedule, 0.03 warm-up ratio, and 16.384-token maximum sequence length. Training is conducted on 64 Ascend H910B-64G hardware.
Evaluation benchmarks. We evaluate performance marks of varying difficulty: AIME (24&25, competition-leve algebraic problems). AMC23 (pre- collegiate mathematics), MATH500 [44 45) (diverse mathematical problems), and GSMSK [47] (grade school math word problems). For inference, we use vLLM [48] with temperature 0.7, generat- ing 8 samples per question and reporting Average accuracy. All inference performed on NVIDIA A100 GPUs
four mathematical reasoning bench-
4.2 Main results
Table [ presents the performance of models trained with our Self-Braking Tuning approaches compared to baselines trained on the unmodified dataset. We observe several significant trends:
Substantial token reduction with preserved acc. Both SBT variants achieve remarkable reduc- tions in token consumption while maintaining comparable accuracy to baseline models. For the 1 model, SBT-E and SBT-D reduce token usage by 30.7% and 23.0% respectively, with accuracy drops of only 2.65% and 1.95%. Even more impressively, when applied to the Llama-3.1 1--------IIsttttee model, SBT-E reduces token consumption by 62.8% while preserving 94. % of the baseline accuracy.
Scaling dynamics across model types Efficiency gains from Self-Braking Tuning (SBT) vary by model type. For general- sereral-purpose purpose models like Llama, larger models benefit more token reductions
'The OpenRI Mathh dataset is licensed under the Apache 2.0 License. We adhere to its terms of use and do redistribute the dataset but build upon it for experimental purposes.
7

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Table I: Performance different models with Self- Self-Braking Tuning applied, evaluated across GSM8K, MATH500, AMC23, and AIME (including AIME24 and AIME25) benchmarks.
GSM8K
MATH500
AIME
AMC23
Average
Base Model
Method
Acc
#Tok
Acc
#Tok
Acc
#Tok
Acc
#Tok
Acc
#Tok
Qwen2.5-Matt 1.5B-Instruct
Bascline
85,00
514
80.25
1712
16.25
7381
55.94
3503
59.36
3277
SBT-E
84,85
426
77.10
1121
13.75
3101
55.63
2044
57.83
1673
SBT-D
84.87
414
77.30
1046
14.17
3381
50.31
1888
56.66
1682
Qwen2.5-Math-
Baseline
96.11
1460
92.67
3816
40.83
11904
83.13
6937
78.19
6029
SBT-E
95,45
997
90.77
2501
38.75
8772
77.19
4443
75.54
4178
7B-Instruct
SBT-D
95.37
956
91.15
2629
38.38
9778
80.06
5208
76.24
4643
Baseline
41.85
1639
25.22
6624
1.25
13150
9.38
10210
19.43
7906
Llama-3.2-
SBT-E
39.96
1056
24.35
3180
0.42
6615
9.06
4708
18.45
3890
I--suutttt
SBT-D
41.21
698
25.07
2591
1.04
6821
13.13
4388
20.11
3624
Baseline
88.03
1593
59.98
9304
9,58
13663
36.75
9742
48.59
8576
Llama-3.1-
8B Instrutt
SBT-E
85.03
777
57.60
2292
6.84
5658
33.44
4045
45.73
3193
SBT-D 88.27
997
62.60
3847
7.70
5845
38.12
6476
49.17
4291
improve from 54.2% (1B) 62.8% 9 (8B). In math -ath-specialized models, however, larger models smaller gains (30.7% for 7B vs. 48.9% for 1.5B). suggesting that specialized models already have more focused and efficient reasoning, leaving less room for further compression. These findings indicate that the self-regulation benefits from SBT depend not only on model scale but also on whether the model is trained for general-purper
domain-specife tasks.
SBT-E vs. SBT-D performance. The two proposed variants show distinct performance characteris- tics. SBT-E generally achieves greater token reductions (averaging 48.3% across all models compared to 43.9% for SBT-D) but with slightly larger accuracy drops. SBT-D demonstrates more balanced performance, particularly the most challenging AIME and MATH500 benchmarks. Notably, for the 1.ma-331-8B model, SBT-D actually improves accuracy on MATH500 by 2.62 % while reducing tokens by 58.7%, suggesting that dynamic truncation may help eliminate not just redundant reasoning but potentially harmful overthinking in some cases.
Analysis
5.1 Impact of overthinking thresholds
The threshold for classifying overthinking instances significantly impacts both dataset composition and model performance. We experimented with thresholds of 0.2, 0.3, and 0.4, which classified approximately 60%, 50%, and 40% of samples as overthinking cases, respectively. As shown in Table 2 a threshold of 0.2 yields the best performance for SBT-E, achieving an optimal balance between token reduction (49% fewer tokens than baseline) and accuracy preservation (97.4% of baseline accuracy).
This finding reveals a crucial insight: aggressive overthinking identification (lower thresholds) leads to more substantial efficiency gains without proportional accuracy losses. This suggests that a significant portion of reasoning in LRMs truly is redundant and can be eliminated without compromising problem-solving capabilities. The consistent pattern across both SBT variants indicates that identifying and addressing overthinking in approximately 60% of reasoning instances represents an optimal operating point for Self-Braking Tuning.
5.2 Preserved reasoning and redundancy masking trade-off
To develop effective self- fiffbbrakig behavior, models learn both when to continue reasoning and when to stop. We investigated different configurations preserved (unmasked) and masked content to understand this balance. Shown in T preserving two complete solutions while masking only a few additional sentences yields optimal performance, reducing tokens by 49% while maintaining 97.4% of bascline accuracy.

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Table Performance across over- think score thresholds. Detailed results shown in Appendix Table 12
Table Performance with different configurations of pre- served reasoning and masked redundant content. Detailed results shown in Appendix Table
13
Method
Threshold
Acc
#Iok
Reservations & Masked Content
Acc
#Tok
Baseline
59.36
3278
Baseline
59.36
3277
SBT-Exaat
0.2
$7.83
1673
0.3
56.70
1755
solution few sentences
56.95
1700
0.4
57.38
1834
1 solution & 1 solution
57.69
1697
SBT-Dynamic
0.2
56.66
1682
solutions A few sentences
57.83
1673
0.3
57.47
1917
0.4
57.36
1902
2 solutions & 1 solution
57.45
1684
This finding provides two key insights. First, solution repetition serves natural termination signal: when model derives the same answer twice, it learns this is a strong indication to conclude reasoning. Second, observe inverse relationship between preserved and masked content: with more preserved reasoning (two solutions), less masked content is optimal; with less preserved reasoning (one solution), more masked content performs better.
This relationship suggests that models require a certain "reasoning quota" to develop robust problem- solving capabilities, which can be satisfied either through more preserved reasoning or more exposure masked reasoning patterns. However, the superior performance of the "two solutions with minimal masked content" configuration indicates that clearly delineated, complete reasoning paths provide stronger learning signals than exposure to additional masked content.
5.3 With vs. without masked redundant thinking
To assess masked redundant thinking (MRT), compare SBT- with and without masked segments. the ablation variant, trajectories truncate directly without retaining masked content.
Table 4: MRT comparison. De- tailed results shown in Appendix Table I4
Results show removing MRT yields minimal accuracy gain (+0.19%) but substantial efficiency loss (+37.8% tokens). With- out exposure to masked overthinking patterns, models fail to internalize what constitutes redundancy. Direct truncation pro- vides only implicit stopping signals-models models see where reason- ing ends but not why. Masked segments serve explicit neg- ative examples: models observe overthinking without gradient reinforcement, enabling discriminative learning of termination boundaries. This exposure- exposure-without- without- reinforcement principle proves essential for self-regulation while preserving reasoning quality.
Config.
Acc
#Tok
Baseline
59.36
3277
w/ MRT
57.83
1673
w/o MRT
58.02
2306
5.4 Impact of 3 on RER-OMR balance
The overthink score (Equation 3) combines structural efficiency (1 77,) and linguistic markers through weighting parameter 3. Table examines sensitivity across B E {0.05, 0.1, 0.15, 0.2.)
Table 5: Performance with different / val- ues for overthink score weighting. De- tailed results shown in Appendix Table [5
Method
Acc
#Tok
Results show 3 0.1 consistently achieves optimal performance for both SBT variants, with 57.83% accu- racy (SBT-E) and 56.66% (SBT-D) while maintaining strong efficiency (1673 and 1682 tokens). Lower values (3 0.05) over-emphasize linguistic markers, degrad- ing accuracy by 0.4 points despite minimal token savings. Higher values (3 2 0.15) reduce linguistic con- tribution, causing accuracy drops of 2.0 points with increased token consumption. This validates prioritiz- ing structural efficiency (90%) while retaining linguistic signals (10%) for robust detection without prompt sen- sitivity.
Baseline
59.36
3277
0.05
56.48
1762
0.1
57.83
1673
SBT-E
0.15
56.52
1874
0.2
55.86
1809
0.05
56.24
1678
0.1
56.66
1682
SBT-D
0.15
56.21
1784
0.2
55.74
1814

---

5.5 Step-level vs. token-level overthinking detection
The granularity of overthinking detection, whether operating at the reasoning step level or token level. impacts both the coherence of preserved reasoning and overall model performance. We compared our step-level approach with token-level alternative using a token efficiency ratio defined as T7 7T where FT represents tokens until first correct answer and TT represents total tokens.
Results in Table demonstaattt that step-level detection outperforms token-level approaches, achieving both higher accuracy and lower token usage. This confirms our hypothesis that reasoning coherence better preserved when entire logical steps are maintained intact. Token-level truncation, while more granular, risks breaking logical units of reasoning, potentially creating disjointed or incomplete thinking patterns that are harder for models learn from or reproduce effectively.
This finding highlights the importance of respecting the inherent structure of reasoning when devel- oping overthinking mitigation strategies: models benefit from complete logical units rather than more aggressive but potentially incoherent truncation approaches.
Table 6: Overthinking detection granularity com- parison. Detailed results Appendix Table
Table 7: Guiding mode comparison. Detailed results shown in Appendix Table 17
Level
Acc
#Tok
Guiding Mode
Acc
#Tok
Baseline 59.36
3277
Baseline
59.36
3277
Step-Level
56.66
1682
Natural Language
56.66
1682
Token-Level
56.24
1753
Special Token
56.61
1797
No Guidance
56.39
1801
5.6 Natural language guidance vs. alternative approaches
A fundamental aspect of Self-Braking Tuning is the mechanism used to signal reasoning termination. We compared our natural language guidance approach (epiphany sentences like "I've verified my answer, no need to continue...") with both special token approach using sstop_overthinkingg and configuration without any explicit guidance.
Table 7 shows natural language guidance achieves optimal performance (56.66% accuracy, 1682 tokens). The necessity of explicit termination signals is confirmed by the no-guidance ablation, which degrades both accuracy (-0.27%) and efficiency (+7.1% tokens). Compared to special tokens (56.61% %, 1797 tokens), natural language guidance maintains comparable accuracy with 6.4% fewer tokens by leveraging models' existing semantic understanding of logical transitions rather than requiring learning of artificial control conventions.
Conclusion
In this paper, propose novel endogenous approach, Self--raakee Tuning (SBT), to mitigating overthinking in large language models. SBT aims to stimulate the model's 's ability to autonomously identify and stop redundant reasoning. We construct a data framework with adaptive reasoning lengths, where overthinking characteristics are extracted through step-level analysis and keyword annotation. Based on this, we design two data generation strategies, SBT-E and SBT-D, to help the model learn when to stop and how to simplify its reasoning process. During supervised fine-tuning, introduce redundancy masking and epiphany sentences to preserve the reasoning paradigm while enhancing the model's sensitivity to redundant thought patterns. Experimental results show that SBT models sigmificantly reduce token consumption by 30%-60% on multiple mathematical benchmarks, with minimal impact on accuracy. Our work demonstrates the potential for large reasoning models to self-regulate their reasoning and offers a promising direction for more efficient long-chain reasoning.
10

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Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 62376245), National Key Research and Development Project (No. 2024YFB3312900, the Key Research and Development Program of Zhejiang Province, China (No. 2024C03255), the Fundamental Research Funds for the Central Universities 226-2024-00170)), MOE Engineering Research Center of Digital Library, CCF-Tencent Rhino-Bird Open Research Fund, and ZJU Kunpeng&Ascend Center of Excellence.
References
OpenR1 Team. -Math-22t Dataset. OpenR1-Math-225 220k 2025.
[2] An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu. Chang Zhou, Chengpeng Chengyuan Li, Dayiheng Liu, Fei Huang, et al. Qwen2 technical report. arXiv preprint arXiv:2407.10671, 2024.
[3] Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed EI- Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. Openai ol system card, 2024.
[4] Daya Guo, Dejian Yang. Haowci Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepscek-rl: Incentivizing reasoning capability in ilms via reinforcement learning. arXiv preprint arXiv:2501. /2948, 2025.
[5] Team Qwen. Owq: Reflect deeply on the boundaries of the unknown. httpp:://qpenmn.gitatt io/ blogg/vq-32b-babobo preview/ 2024.
[6] Google DeepMind. Gemini flash thinking.
flash thashhthinning//// 2025.
[7] Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du Chonghua Liao, et al. Kimi kl. 5: Scaling reinforcement learning with Ilms. arXiv preprint 250: /2599, 2025.
[8] Ehsan Latif, Yifan Zhou, Shuchen Guo, Lehong Shi, Yizhu Gao, Matthew Nyaaba, Ame Bewerdorff, Xiantong Yang, and Xiaoming Zhai. Can openai ol outperform humans in higher-order cognitive thinking? arXiv preprint 255.52241..05753, 2024.
[9] OpenAL.
Learning
reason
with
Ilms.
https://openaa..om/mnne
learning ce-reeoon--... with 11ms/
2024.
[10] Yue Wang, Qiuzhi Liu, Jiahao Xu, Tian Liang, Xingyu Chen, Zhiwei He, Linfeng Song, Dian Yu, Juntao Li, Zhuosheng Zhang, et al. Thoughts are all over the place: On the underthinking of ol-like 1lms. arXiv preprint arXiv 18585, 2025.
[11] Yuyang Wu, Yifei Wang, Tianqi Du, Stefanie Jegelka, and Yisen Wang. When more is less: Understanding thain-of-thought length in lmss. In Workshop Reasoning and Planning for Large Language Models.
[12] Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang. Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, et al. not think that much for 3=? on the overthinking ol-kike 1lms. arXiv preprint arXiv:2412.211877 2024.
[13] Yang Sui. Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Hanjic Chen, Xia Hu, et al. Stop overthinking: A survey on efficient reasoning for large language models. arXiv preprint arXiv:2503. 2503.1999.,, 2025.
[14] Rui Wang, Hongru Wang, Boyang Xue, Jianhui Pang. Shudong Liu, Yi Chen, Jiahao Qiu, Derek Fai Wong. Heng Ji, and Kam-Fai Wong. Harnessing the reasoning economy: A survey of efficient reasoning for large language models. arXiv preprint arXiv:2503. 25732477777 2025.
[15] Pranjal Aggarwal and Sean Welleck. LI: Controlling how long a reasoning model thinks with reinforcement learning, 2025.
[16] Haotian Luo, Li Shen, Haiying He, Yibo Wang, Shiwei Liu, Wei Li, Naiqiang Tan, Xiaochun Cao, and Dacheng Tao. O1 O1-pruee:: Length- harmonizing fine-tuning tuning for ol like reasoning pruning. arXiv preprint 17X1v:2501.12570, 2025.

---

[17] Edward Yeo, Yuxuan Tong, Monry Niu, Graham Neubig, and Xiang Yue. Demystifying long chain-of- thought reasoning in llms. arXiv preprini urXiv:2502.03373. 2025.
[18] Wenjie Ma, Jingxuan He, Charlie Snell, Tyler Griggs, Sewon Min, and Matei Zaharia. Reasoning models can be effective without thinking. arXiv preprint arXiv:2504 09858, 2025.
[19] Chenxu Yang. Qingyi Si, Yongiee Duan, Zheliang Zhu, Chenyu Zhu, Zheng Lin, Li Cao, and Weiping Wang. Dynamic early exit in reasoning models. arXiv preprint arXiv:2504. 11.50041580 15895, 2025.
[20] Jintian Zhang, Yugi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, and Ningyu Zhang. Lightthinker: Thinking step-by-step compression. arXiv preprint urXiv:2502.15589 2025.
[21] Tingxu Han, Zhenting Wang, Chunrong Fang. Shiyu Zhao, Shiqing Ma, and Zhenyu Chen. Token-budget- aware IIm reasoning. arXiv preprint arXiv:2412. 185477 2024.
[22] Silei Xu, Wenhao Xie, Lingxiao Zhao, and Pengeheng He. Chain of draft: Thinking faster by writing less. arXiv preprint arXiv:2502. 18600, 2025.
[23] Hugging Face. Open rl: A fully open reproduction of deepseek-rl, January 2025.
[24] NovaSky Team. Sky-tl: Fully open-source reasoning model with ol op-preveew performance in $450 budget, 2025,
[25] Yixin Ye, Zhen Huang. Yang Xiao, Ethan Chern, Shijie Xia, and Pengfei Liu. Limo: Less is more for reasoning. arXiv preprint 2502:22500.03387.. 2025.
[26] Haolei Xu, Yuchen Yan, Yongliang Shen, Wenqi Zhang, Guiyang Hou, et al. Mind the gap: Bridging thought leap for improved afain-of-thougle tuning. May 2025.
[27] Yuchen Yan, Yongliang Shen, Yang Liu. Jin Jiang. Xin Xu, et al. Mathfimer: Enhancing mathematical reasoning by expanding reasoning steps through fill-in-the middde task, February 2025.
[28] Haolei Xu, Xinyu Mei, Yuchen Yan, Rui Zhou, Wenqi Zhang, et al. Easysteer: A unified framework for High-performancee and extensible IIm steering. September 2025.
[29] Yuchen Yan, Jin Jiang, Yang Liu, Yixin Cao. Xin Xu, Mengdi Zhang, Xunliang Cai, and Jian Shao. S 3emath: Spontaneous step-level self-correction makes large language models better mathematical reasoners. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 25588 25596, 2025.
[30] Yuchen Yan, Yongliang Shen, Yang Liu, Jin Jiang. Mengdi Zhang, Jian Shao, and Yueting Zhuang. Inflythink: Breaking the length limits of long-context reasoning in large language models. arXiv preprint arXiv:2503.0m 066992 2025.
[31] Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi, Wenjing Zhang, Jiangze Yan, Ning Wang, Kai Wang. and Shiguo Lian. Dast: affiiculty-ddapivv slow-thinking for large reasoning models. arXiv preprint arXiv:2503.0442 2025.
[32] Ping Yu, Jing Xu, Jason E Weston, and Ilia Kulikov. Distilling system 2 into system 1. In The First Workshop on System-2 Reasoning at Scale, 244
[33] Tergel Munkhbat, Namgyu Ho, Seo Hyun Kim, Yongjin Yang, Yujin Kim, and Se- Se-Young Yun. Self-training elicits concise reasoning in large language models. CoRR, 2025.
[34] Yu Kang, Xianghui Sun, Liangyu Chen, and Wei Zou. C3ot: Generating shorter chain-of-thought without compromising effectiveness. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 24312 243200, 2025.
[35] Heming Xia, Yonggi Li, Chak Tou Leong, Wenjie Wang, and Wenjie Li. Tokenskip: Controllable chain-of-thought compression in llms. arXiv preprint arXiv:2502. /2067, 2025.
[36] Xinyin Ma, Guangnian Wan, Runpeng Yu, Gongfan Fang, and Xinehao Wang. CoT-valve: Length- compressible tf-in-of-though tuning. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Moham- mad Taher Pilehvar, editors, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6025-6035, Vienna, Austria, July 2025. Association for Computational Linguistics.
[37] Baohao Liao, Yuhui Xu, Hanze Dong, Junnan Li, Christof Monz, Silvio Savarese, Doyen Sahoo, and Caiming Xiong. Reward- guided speculative decoding for efficient Ilm reasoning. In second International Conference on Machine Learning.
12

---

[38] Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan, Xinglin Wang, Bin Sun, Heda Wang, and Kan Li. Escape sky-high cost: Early-stopping self-consistency for multi-step reasoning. In The Twelfth International Conference on Learning Representations.
[39] Rohin Manvi, Anikait Singh, and Stefano Ermon. Adaptive inference- time compute: Lims can predict if they can do better, even generatiin arXiv preprint arXiv:4410.02255. 2024.
[40] Anqi Zhang, Yulin Chen, Jane Pan, Chen Zhao, Aurojit Panda, Jinyang Li, and He He. Reasoning models know when they'n 'e right: Probing hidden states for self- verffveriiic,tinn. arXiv preprint arXiv:2504.054 05419, 2025.
[41] Ayeong Lee. Ethan Che, and Tianyi Peng. How well llms compress their own chain-of- -hhin-oftthoughh? a token complexity approach. arXiv preprint arXiv:2503.01 0411, 2025.
[42] Matthew Renze and Erhan Guven. The benefits of a concise chain of thought on problem- solving in large language models. In 2024 2nd International Conference on Foundation and Large Language Models (FLLM), pages 476 IEEE, 2024.
[43] Simon A Aytes, Jinheon Back, and Sung Ju Hwang. Sketch-of-thought: Efficient Ilm reasoning with adaptive cognitive-inspieed sketching. arXiv preprint 0003::2503.05579,20000
[44] Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103 100.03344 2021.
[45] Hunter Lightman. Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let's verify step by step. In The Twelfth International Conference on Learning Representations. 2023.
[46] Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al- Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. arXiv preprint 240i:2407.2178. 2024.
[47] Karl Cobbe. Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reicchir Nakano, et al. Training verifiers to solve math word problems, arXiv preprint ar 10. 2021.
[48] Woosuk Kwon, Zhuohan Li, Siyuan Zhuang. Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th Symposium Operating Systems Principles, pages 61 1-626, 2023.
[49] Aryo Pradipta Gema, Joshua Ong Jun Leang, Giwon Hong, Alessio Devoto, Alberto Carlo Maria Mancino, Rohit Saxena, Xuanli He, Yu Zhao, Xiaotang Du, Mohammad Reza Ghasemi Madani, et al. Are we done with mmlu? In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume I: Long Papers), pages 9066, 2025.
[50] David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman. Gpqa: A graduate-level google-proof q&a benchmark. In First Conference on Language Modeling, 2024.
[51] Ximing Lu, Seungju Han, David Acuna, Hyunwoo Kim, Jaehun Jung, Shrimai Prabhumoye, Niklas Muennighoff, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro. et al. Retro-search: Exploring untaken paths for deeper and efficient reasoning. arXiv preprini arXiv:2504.04330 2025.
[52] Chenrui Fan, Ming Li. Lichao Sun, and Tianyi Zhou. Missing premise exacerbates overthinking: Are reasoning models losing critical thinking skill? arXiv preprint arXiv:2504. S0W.22544.06144. 2025.
[53] Dacheng Li, Shiyi Cao. Tyler Griggs, Shu Liu, Xiangxi Mo, Eric Tang, Sumanth Hegde, Kourosh Hakhamaneshi, Shishir Patil, Matei Zaharia, al. Llms can easily learn reason from demonstrations structure, not content, is what matters! arXiv preprint arXiv:2502.05 07374, 2025.
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A
Limitations
While Self- Self-Braking Tuning (SBT) has demonstrated its ability reduce overthinking enabling models autonomously regulate reasoning length, several limitations remain:
Domain generalization. Our study focuses primarily on math reasoning tasks (e.g.. GSM8K, MATH). which, while structurally rich and sensitive overthinking, challenges. The applicability SBT to open-ended, commonsense, logical, or multimodal reasoning remains unverified. These domains may exhibit distinct redundancy patterns that require specialized adaptation strategies.
cover the full diversity reasoning
Data scalability and adaptivity. Due to computational constraints, our experiments are conducted on datasct of approximately 92K examples. The impact of scaling larger datasets (c.g., millions of samples) remains unexplored. Moreover, current data construction strategies (SBT-E/D) rely fixed threshold parameters for overthinking detection, which may require manual tuning across different tasks and hinder dynamic adaptation.
Overthinking signal definition. We use reasoning efficiency (n=) and overthink marker ratio (Ne)) to estimate redundancy, hut these metrics may not capture all subtle or latent forms of reasoning utility. Some steps that appear redundant may actually serve hidden purpose, such as helping the model organize its reasoning implicitly represent abstract patterns.
Interpretability and controllability. While SBT introduces soft constraint via loss masking, the internal process by which a model decides to terminate reasoning remains opaque. There no explicit mechanism trace or control this decision, limiting transparency and reliabiliy-especiiae in applications requiring high interpretability, such as education, healthcare, or scientific domains.
Potential trade-offs complex tasks. For tasks requiring deep, multi-step reasoning (c.g., theorem proving). prematurely terminating reasoning may risk omitting critical steps. Although SBT preserves accuracy most cases, it may underperform in scenarios where full-chain reasoning is essential. Adaptive or progressive braking strategies may be needed better balance efficiency and completeness.
Future work could address these limitations by (i) expanding SBT to open-ended and multimodal tasks, (ii) scaling to larger and more diverse datasets, (iii) developing adaptive thresholding and automatic data construction pipelines, (iv) improving multilingual and nomain-robus prompt designs, and (v) integrating interpretable or feedback-driven stopping mechanisms that better align with complex reasoning dynamics.
B Autonomous reasoning regulation in self-braking models
A critical distinction of our Self-Braking Tuning framework is that SBT-E and SBT-D are data construction strategies, inference-time control mechanisms. After training, models exhibit autonomous reasoning regulation without external intervention, dynamically adapting reasoning depth to problem complexity.
B.1 Emergent self-regulation behavior
To validate that self- braking emerges naturally during inference, we analyzed reasoning patterns of Qwen2.5- Math-7BB-nsstuut--BB--- on AIME. Models spontancously generate epiphany sentences (e.g., "Wait, I've verified my answer. No need to continue thinking.") and terminate without external signals.
Table 8: Analysis of autonomous early termination in SBT-trained models on AIME. Models naturally terminate reasoning in about 50% of cases, achieving both higher accuracy and 48-51% token reduction.
Dataset
Exit Type
of Cases
Acc
#Tok
Early Exit
50.83%
41.80%
5,692
AIME24
No Early Exit
49.17%
38.98%
11,084
Early Exit
49.17% 50.83%
41.53%
6.483
AIME25
No Early Exit
32.79%
12,201
Table TableeBshowe shows that self- braki-brking is not enforced through hard constraints. Cases with early termination achieve higher accuracy while using fewer tokens -iniicatin models learn to recognize when additional reasoning becomes counterproductive.
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B.2 Problem-adaptive reasoning depth
SBT-trained models autonomously adjust reasoning length based on task difficulty:
Adaptive reasoning depth of The x.3x variation between GSM8K and AIME25 demonstrates autonomous complexity adaptation without external difficulty indicators.
Table 9:
across difficulty levels.
Dataset
Difficulty
Avg Steps
GSM8K
Easy
27.78
MATH500
Medium
51.32
AMC23
Hard
106.40
AIME25
Very Hard
202.23
substantial variation (27.78 to 202.23 steps) occurs without external difficulty indicators task-specific prompting, suggesting internalized problem complexity assessment.
B.3 Interpretation of self-regulation mechanisms
The training process exposes models three key signals: (1) preserved reasoning demonstrating sufficient problem-solvin depth, (2) natural language cues marking reasoning completion, and (3) masked redundant segments that models observe but do not Iearn to reproduce. This combination appears to develop an internal representation of "reasoning sufficiency" that activates during generation.
We hypothesize that the natural language braking prompts (e.g.. "Wait, I've gotten the same answer multiple times") serve anchor points in the representation space, allowing models to recognize similar reasoning states during inference and trigger corresponding termination behavior. The consistency of early termination rates (~-50% on AIME) across individual problems suggests this behavior generalizes beyond memorization of training examples.
C Out-of-domain generalization
To evaluate whether Self-Braking Tuning generalizes beyond mathematical reasoning, test SBT-trained models out-of-domain benchmarks covering diverse knowledge domains: MMLU-Redux (general knowledge and reasoning) 40] GPQA-Diamond (graduate-Ievel science questions) [SO] All models were trained exclusively on math data (OpenRI (openRI-Math)) MMath),, with no -epain-specific fine-tuning for these benchmarks.
Table 10: Out-of-domain evaluation MMLU-Redux and -Diamodd. Models trained ex- clusively on mathematical reasoning data demonstrate consistent efficiency gains (26-65% token reduction) across non-mathematical tasks, with accuracy drops typically limited 1-3%.
MMLu-Redux
GPQA-Diamond
Average
Base Model
Method
Acc
#Tok
Acc
#Tok
Acc
#Tok
Baseline
45.84
2,061
24.75
5,485
35.30
3,773
Qwen2.5-Man -athh----
SBT-E
43.12
1,403
25,06
3,194
34.09
2.299
SBT-D
43.28
1,566
26.20
3,002
34.74
2,284
Baseline 67.04
3,229
41.15
8,892
$4.10
6.061
Qwen2.5-Math-7B
SBT-E
65.84
1,927
40.40
6,205
53.12
4,066
SBT-D
66.39
1,998
41.29
6,706
53.84
4,352
Baseline SBT-E
35.62
1,933
17.99
9,321
26.81
5,627
Llama-3.2-1B
32.24
770
24.24
3,516
28.24
2,143
SBT-D
33.12
725
23.48
3,157
28.30
1,941
Baseline 80.53
2,481
37.31
8,918
58.92
5,699
Llama-3.1-88
SBT-E 77.46
1,646
36,30
5,346
56.88
3,496
SBT-D
77.74
1,668
36.91
6,717
57.33
4,192
Cross-domain transferability. SBT achieves 26 65% token reduction across non man--aattmttcal benchmarks de- spite training exclusively on math data, with minimal accuracy degradation (typically 1 1-3%). This demonstrates that self-regulation learned from mathematical reasoning generalizes to diverse knowledge domains.
15

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Model size effects. Efficiency gains scale inversely with model size: smaller models (Llama-3.2 3.2--a---2--11 3.) achieve 65% token reduction, while larger models ILlama-3.188B. Qwen2.5-Mathe
show 26-40% reductions. Notably. Llama-3.2-1 with SBT-E improves GPQA-Diamon accuracy by 6.25 percentage points (17.99% - 24.24%) indicating that overthinking mitigation can enhance performance on challenging out-of-domain tasks for smaller models.
Architecture consistency. Both Nomain-specializee (Qwen2.5-Maid) and general-puue purpose (Llama) models benefit comparably from SBT out-of-lomain evaluation, confirming that self-braking represents transferable meta-cognitive capability rather than task-specific adaptation.
These results establish that reasoning efficiency patterns learned from mathematics transfer broadly to general knowledge and scientific reasoning, supporting SBT's applicability beyond training domain.
D Extended threshold analysis
To comprehensively evaluate the robustness of our overthinking detection mechanism, we conducted an extended threshold analysis spanning 71 E {0.05, 0.2, 0.3, 0.4, 0.5). This analysis examines how threshold selection affects both the proportion of samples classified overthinking and the resulting model performance.
Table 11: Extended threshold analysis for overthinking score across E [0.05, 0.5]. Results Averaged across GSM8K, MATH500, AIME, and AMC23, Overthink indicates the proportion of samples classified as containing overthinking each threshold.
Method
Threshold
Overthink %
Accuracy
#Tokens
Reduction
Baseline
59.36
3,277
0.05
75.20
55.14
1,407
57.1%
0.1
65.40
55.59
1,427
56.5%
0.2
60.30
57.83
1,673
48.9%
SBT-E
0.3
50.20
56.70
1,755
46.4%
0.4
41.00
57.38
1,834
44.0%
0.5
2.06
57.12
2,602
20.6%
0.05
74.20
54.90
1,215
62.9%
0.1
62.30
55.90
1,251
61.8%
0.2
62.50
56.66
1,682
48.7%
SBT-D
0.3
50.90
57.47
1,917
41.5%
0.4
40.10
57.36
1,902
42.0%
0.19
57.09
2,696
17.7%
D.1 Threshold regime characterization
The extended analysis reveals three distinct performance regimes:
Aggressive pruning (71 0.2). Low thresholds classify 62-75% of samples as overthinking, achieving maximum token reduction (57-63%) but incurring significant accuracy degradation (2.5-4.5 percentage points). At T = 0.05, the framework removes reasoning content too aggressively, truncating even necessary exploration steps in complex problems. This regime prioritizes efficiency over reasoning completeness.
Balanced operation (r) E [0.2, 0.4]). This range identifies 40-62% of samples as overthinking and demonstrates stable performance with < 1.2% accuracy variation while maintaining 41-49% token reduction. The relative insensitivity to exact threshold values within this range indicates robust overthinking detection. Peak performance occurs at 7; = 0.2 for both SBT variants, achieving optimal accuracy-efficient tradc-offs 55.883% 48.9% reduction for SBT-E; 56.66% / 48.7%% for SBT-D).
Conservative pruning (n = 0.5). High thresholds classify only 0.2-2% of samples as overthinking, causing the framework to degenerate toward baseline behavior. Taken reduction drops to 18-21% % with compensatory accuracy gains, indicating that insufficient overthinking mitigation fails to realize efficiency benefits.
D.2 Threshold selection rationale
We select 0.2 the default threshold based three considerations:
16

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Performance optimality, This threshold achieves the best accuracy within the balanced regime while maintain- ing substantial efficiency gains across both SBT variants and all evaluated benchmarks.
Empirical stability. The plateau in the [0.2, 0.4, range demonstrates that performance degrades gradually rather than catastrophically under threshold perturbations, supporting practical deployability.
Dataset composition. threshold 0.2 creates approximately 60% SBT-processed and 40% original tra- jectories, enabling models to Iearn both when to terminate (from processed examples) and when to continuc reasoning (from preserved examples). This balanced exposure prevents gerggenrraliztionn of braking behavior while establishing clear termination patterns.
These findings establish that SBT's effectiveness stems from systematic overthinking identification rather than aggressive truncation, with the optimal operating point balancing reasoning preservation against redundancy elimination.
E Overthink markers
In the study of overthinking behaviors, several prior works have highlighted that certain words associated with reflection, hesitation, or backtracking play critical role in identifying and guiding redundant reasoning Building on these insights, we compile a set of common Overthink Markers, including:
processes 17 5T.33.
Another Check
Backtrack
But
Going back
Hmm
Hmmm
However
Hold on
Instead of Let check
Just to be thorough
Just to make sure
Let me just double-check
Let me try another Maybe can consider
Let me verify
Maybe
Maybe should consider
Might
Not sure Retry
Perhaps
Recheck
Trace back
Wait
These terms frequently co-occur with behaviors such as repeated verification, alternative hypothesis formulation, reasoning path retracing, and are therefore treated as linguistic indicators of redundant cognitive load during reasoning. Based this, we construct the set M as part of our overthinking detection metrie, used to compute redundancy density at the linguistic level and help identify potential efficiency bottlenecks in deep reasoning.
F Algorithms of self-braking tuning
To facilitate reproducibility and offer transparent view into data construction pipeline. provide the formal procedures for the Self- Braking Tuning Exact (SBT-E) and Self-Brakin Brakinr Tuning Dynamic (SBT-D) methods in this appendix. These two strategies represent complementary approaches for curating training data that teach models to regulate their own reasoning length and avoid excessive computation.
SBT-E adopts uniform truncation scheme, where cach reasoning trajectory is truncated at consistent structural boundary: the Foundation Solution and the first Evolution Solution are preserved, followed by small masked portion of subsequent reasoning. This approach provides clean and interpretable signal for identifying the onset overthinking.
contrast, SBT-D offers a fine-grained, adaptive strategy that dynamically determines the optimal stopping point for each problem based on the model's own overthinking scores. It incrementally evaluates reasoning steps, retaining those below a primary overthinking threshold, and masks additional steps that exceed this threshold but remain below secondary cutoff effectivvey preserving moblem-specifte nuances in reasoning depth.
Crucially, both methods employ loss masking the redundant segments: the model patterns during training but receives no gradient updates from them. This enables the model to implicitly recognize and leamn to avoid overthinking, without being rewarded for producing verbose or unnecessary steps.
these overthinking
The algorithmic details are outlined in Algorithms and which serve as blueprint for implementing the Self-Braking Tuning framework.
17

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Algorithm Self-Braking Tuning Exact (SBT-B)
Require: Reasoning trajectory T with Foundation Solution FS and Evolution Solutions ES [ES ...
Ensure: Modified trajectory T" with preserved and masked segments PreservedSegment +- FS ES1
2: if [ES] > then
3: MaskedSegment <- First 10-20% of ES2
4: else
5:
MaskedSegment <
6: end if
7: +- PreservedSegment + MaskedSegment (With loss masking on MaskedSegment)
8: return T"
Algorithm 2 Self-Braking Tuning Dynamic (SBT-D)
Require: Reasoning trajectory T with steps [S1. S2, ... Sal, thresholds Ti and 72
Ensure: Modified trajectory T" with preserved and masked segments
1: PreservedThinking +- Foundation Solution from
2: index of first step after Foundation Solution
3: while S and CalculateOverthinkSSoro Ser.eveettiinssosoeeeeeeeeeeiiin (resseredTThinking S.) < Tı do
4: PreservedThinking +- PreservedThinking + S.
5: F-i++ i++
6: end while
7: MaskedThinking +
8: while i S and CulellecDvetiinksooo//eeeerrhrhn + MaskedThinking + S.) T2
do
9: MaskedThinking
MaskedThinking + S²
10: i- 1-i+1
11: end while
12: +- PreservedThinking + MaskedThinking (With loss masking on MaskedTThinee (iikkinng)
return 7"
G Detailed experimental results
In Section [] to improve the readability of the main text, we only present the Average results across the datasets. Here, provide the specific data and evaluation results.
Table 12: Performance with varying overthink score thresholds. Lower thresholds (0.2) achieve optimal efficiency- -cccuracc trade-offs-up to 49% token reduction while preserving 97.4% baseline aggressive pruning effectiveness. Accuracy remains stable across thresholds on simple tasks (GSM8K), while complex tasks (AIME) show marginal accuracy gains at higher thresholds (0.3-0.4) with increased token costs, indicating task-dependent sensitivity pruning aggressiveness.
GSM8K
MATH500
AIME
AMC23
Average
Method
Threshold
Acc
#Tok
Acc
#Tok
Acc
#Tok
Acc
#Tok
Acc
#Tok
Baseline
85.00
514
80.25
1712
16.25
7381
55.94
3503
59.36
3277
0.2
84.85
426
77.10
1121
13.75
3101
55.63
2044
57.83
1673
SBT-Exact
0.3
85.16
424
77.25
1113
15.63
3353
48.75
2132
56.70
1755
0.4
84.73
421
77.40
1130
12.71
3795
54.69
1988
57.38
1834
0.2
84.87
414
77.30
1046
14.17
3381
50.31
1888
56.66
1682
SBT- -ynamic
0.3
84.58
410
78.00
1125
12.92
3710
54.37
2422
57.47
1917
0.4
85.07
407
78.73
1187
14.38
3593
51.25
2421
57.36
1902
18

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Table 13: Performance corresponding to different combinations of preserved and masked reasoning. The best configuration preserving two complete solutions while masking only few redundant sentences- sentences-achieves the highest Average accuracy (57.83%) with 49% fewer tokens than baseline. On simpler datasets like GSM8K, even minimal preservation suffices for learning effective termi- nation, while harder tasks such as AIME benefit more from exposing multiple complete solutions, highlighting that task complexity influences the optimal balance between reasoning exposure and truncation cues.
Reservations
GSM8K
MATH500
AIME
AMC23
Average
Masked Content
Acc
#Tok
Acc
#Tok
Ace
#Tok
Acc
#Tok
Ace
#Tok
Baseline
85.00
514
80.25
1712
16.25
7381
55.94
3503
59.36
3277
solution & A few sentences
85.23
416
78.00
1103
12.71
3132
51.88
2148
56,95
1700
1 solution & solution
85,06
432
78.60
1101
13.96
3178
53.12
2076
57.69
1697
2 solutions & A few sentences
84.85
426
77.10
1121
13.75
3101
55.63
2044
57.83
1673
2 solutions & solution
84.77
411
77.82
1034
12.50
3092
54.69
2197
57.45
1684
Table 14: Performance comparison of Masked Redundant Thinking mechanism across benchmarks of varying difficulty. Removing MRT yields marginal Average accuracy gain (+0.19%% but substantial token increase (+37.89 with dataset-dependent effects: simple tasks (GSM8K: +9.6% tokens) show minimal impact, while complex reasoning tasks experience severe efficiency degradation (AIME: +46.5% tokens, AMC: +36.4% tokens). These results confirm MRT enables efficient termination learning through exposure-withhou---eiffrceee
with benefits scaling with task complexity.
GSM8K
MATH500
AIME
AMC
Average
Configuration
Acc
#Tok
Acc
#Tok
Acc
#Tok
Acc
#Tok
Acc
#Tok
Baseline
85.00
514
80.25
1712
16.25
7381
55.94
3503
59.36
3277
w/ MRT
84.85
426
77.10
1121
13.75
3101
55.63
2044
57.83
1673
w/o MRT
85.05
467
78.30
1425
14.38
4544
54.37
2788
58.02
2306
Table 15: Performance with different 3 values for overthink score weighting across benchmarks.
While 5 0.1 achieves best Average performance for both variants (57.83% SBT-E, 56.66% SBT- D), optimal values vary by dataset: 3 0.05 maximizes accuracy on simple tasks (GSM8K) and minimizes tokens on MATH500, while
0.1 dominates on complex tasks (AIME, AMC23). Lower 3 over-emphasizes linguistic markers with limited efficiency gains, while higher 3 degrades both accuracy and efficiency. This validates = 0.1 as the robust default, balancing structural efficiency (90%) and linguistic signals (10%) across task complexities.
GSM8K
MATH500
AIME
AMC23
Average
Method
e
Acc
#Tok
Acc
#Tok
Acc
#Tok
Acc
#Tok
Acc
#Tok
Baseline
85.00
514
80.25
1712
16.25
7381
55.94
3503
59.36
3277
0.05
84.92
433
76.45
1089
13.13
3198
51.42
2328
56.48
1762
0.1
84.85
426
77.10
1121
13.75
3101
55.63
2044
57.83
1673
SBT-E
0.15
84.78
448
76.80
1243
13.54
3487
50.96
2318
56.52
1874
0.2
84.61
441
76.05
1198
12.92
3421
49.86
2176
55.86
1809
0.05
84.89
421
76.50
1087
13.33
3289
50.24
1915
56.24
1678
0.1
84.87
414
77.30
1046
14.17
3381
50.31
1888
56.66
1682
SBT-D
0.15
84.83
437
76.95
1178
13.75
3598
49.31
1923
56.21
1784
0.2
84.71
429
76.30
1154
13.13
3701
48.82
1972
55.74
1814
19

---

Table 16: Performance comparison between step-level and token-level overthinking detection. Step- level supervision consistently achieves lower token usage across all datasets (e.g.. 414 vs. 431 on GSM8K, 1888 vs. 2091 on AMC23), indicating more efficient reasoning truncation. Accuracy-wise the two methods are comparable on GSM8K and MATH500, but step-level clearly outperforms token-level on more challenging tasks such as AIME (14.17% vs. 11.34%) These results highlight that preserving complete reasoning steps enables better efficiency-accurae trade-offss, especially for complex problem-solving.
GSM8K
MATH500
AIME
AMC23
Average
Level
Acc
#Tok
Acc
#Tok
Acc
#Tok
Acc
#Tok
Acc
#Tok
Baseline
85.00
514
80.25
1712
16.25
7381
55.94
3503
59.36
3277
Step-Level
84.87
414
77.30
1046
14.17
3381
50.31
1888
56.66
1682
Token-Level
85.09
431
78.23
1088
11.34
3399
50.31
2091
56.24
1753
Table 17: Performance comparison of different guiding mechanisms for reasoning termination. Natu- ral language guidance consistently achieves the best efficiency-accurane trade-off across benchmarks, with 3381 vs. 3647 tokens on AIME and 1888 vs. 2007 on AMC compared to special tokens. The ablation without any guidance mechanism (No Guidance) demonstrates the necessity of explicit termination signals, showing degraded performance with both reduced accuracy (-0.27%) and increased token consumption (+7.1%) compared to natural language guidance. These results confirm that semantically aligned, self-reflective cues enable more effective reasoning regulation than explicit control tokens or implicit learning alone.
GSM8K
MATH500
AIME
AMC
Average
Guiding Mode
Acc
#Tok
Acc
#Tok
Acc
#Tok
Acc
#Tok
Acc
#Tok
Baseline
85.00
514
80.25
1712
16.25
7381
55.94
3503
59.36
3277
Natural Language
84.87
414
77.30
1046
14.17
3381
50.31
1888
56.66
1682
Special Token
84.94
413
77.92
1120
12.34
3647
51.25
2007
56.61
1797
No Guidance
84.73
421
77.05
1098
13.54
3523
50.24
2162
56.39
1801
20

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NeurIPS Paper Checklist
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Question:
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The abstract and introduction clearly state the main contributions of the paper, which include proposing a novel framework called Self-Braking Tuning (SBT) to address overthinking in large reasoning models. They highlight the methodology for identifying overthinking patterns, constructing adaptive-length reasoning datasets (SBT-E and SBT-D), and introducing braking prompt mechanism. The scope, limitations, and experimental validation on mathematical benchmarks are also discussed, aligning with claims made.
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Justification: We thoroughly discuss the limitations of the proposed Self-Braking Tuning (SBT) framework in Appendix A It outlines five key limitations: domain generalization. data scalability and adaptivity, overthinking signal definition, interpretability and controllability. and potential trade-off's in complex tasks. Each limitation clearly explained with context and implications future research.
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For each thcoretical result, does the paper provide the full of assumptions and complete (and correct) proof?
Question:
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Justification: The paper does not include theoretical results in the form of formal theorems. lemmas. mathematical proofs. It focuses an empirical Seethoodoogy-SSll-BBiiaaae address overthinking in large reasoning models. The contributions are primarily practical, involving datasct construction, model training strategies, and experimental validation. There are no formal theoretical claims requiring assumptions or proofs.
Tuning (SBT)-to
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Justification: The paper does not provide open access to the code or datasets used in the experiments. However, it offers high level of methodological transparency, with detailed descriptions of the Self-Braking Tuning (SBT) framework, including dataset construction strategies (SBT-E and SBT-D), training procedures. evaluation benchmarks, and implementation settings (e-g.. learning rates, model architectures, and inference configurations). The experimental protocols, such as accuracy metrics, token counting methods, and comparison basclines, are clearly specified. As a result, although the actual code and datasets not publicly relcased, the approach is sufficiently well-documented to be replicated by researchers with appropriate resources and technical expertise. Therefore, while open access to code and data is absent, the paper maintains strong reproducibility through comprehensive methodological and experimental reporting.
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The paper specifies the training and test details necessary to understand the results. describes the datascts used (c.g., OpenR 1 1-Math-SB T-E and OpenR O 1-Math-SBT-DD the supervised fine-tuning (SFT) process, model configurations (e.g., Instruct), and training parameters such as the learning rate (5c-5 with cosine decay), maximum sequence length (16,384 tokens), warm-up ratio (0.1), and training framework (Megatron-LMD), Additionally, evaluation protocols are detailed, including benchmarks (GSM8K, MATH500, AIME, AMC23), inference settings (lemperature = 0.7, 8 samples per question), and metrics (accuracy and token consumption). These details provided in Section 4.1
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23

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in Table I and detailed in Appendix Tables tables 12 13 16 and 17 include performance metrics Averaged multiple runs (e-g., accuracy and token counts), which indicate consistency across evaluations. Additionally, the experimental methodology such as sampling 8 oulputs per question with fixed temperature (0.7) ensures controlled variability, supporting the reliability of the reported Averages, These practices align with standard reporting of statistical significance in similar empirical studics.
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Question:
Answer: [NA]
The paper docs not involve the release of pretrained language models, image generators, or scraped datasets that could pose significant risks for misuse. The proposed Sclf- Beff-BBraing Tuning (SBT) framework is methodological advancement aimed at improving reasoning efficiency in large language models by reducing overthinking. It does not introduce new assets-such as generative models datasets with sensitive content-that would require safeguards against misuse. Therefore, specific safeguards are necessary for the responsible release of data models in this work.
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The paper uses the OpenR 1-Math dataset, which is licensed under the Apache 2.0 License. The license and terms of use explicitly mentioned in Section and the full license text is provided in the supplementary material. We comply with the licensing requirements and do redistribute the dataset but use to construct our training examples.
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Answer: [NA]
such datasets, code repositories, or models that would require formal documentation or licensing. builds upon the existing OpenR Math dataset and proposes a novel training framework Self-Braking Tuning (SBT) which is described in detail within the paper. However, the SBT datasets (OpenR M 1-Math-SBT--- and OpenR1- not relcased standalone assets; rather, they are constructed internally for training made publicly available, there accompanying
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The paper does not introduce release any
Math-SBBTL
purposes. Since external-facing documentation provided alongside them.
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Answer: [NA].
The paper does not involve crowdsourcing experiments or research with human subjects. The work focuses on developing novel training framework Seaewwooroo---------ee Self-Braking Tuning (SBT) for large reasoning models, and all experiments
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conducted using existing datasets (e.g., GSMSK, MATH500, AIME, AMC23) and computational methods. There is no mention of data collection from human participants, instructions provided to individuals, or compensation related to human subject involvement. Therefore, this question is not applicable (NA).
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Answer: [NA].
Justification: The paper does not involve crowdsourcing experiments research with human subjects. The work focuses developing novel training framework Self Self-Braking Tuning (SBT) for large reasoning models, and experiments are conducted using existing datasets (e.g.. GSM8K, MATH500, AIME, AMC23) and computational methods. There is no mention of human participation in data collection, model evaluation, or any other aspect of the research. Therefore, the question is not applicable (NA).
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Answer: [Yes].
Justification: The core methodology of the paper involves fine- fine-tuming and optimizing large language models (LLMs) as base models podell--uuche such as Qwen2.5-Math- 1.5t-11.--nnsttucct and the Llama-3 series which constitute central component of the research. According to the NeurlPS 2025 LLM policy, explicit declaration is required when L.M usage forms important, original, non-standard part of the methodology. The paper complies with this requirement by providing a detailed description LLMs used and the tuning methods applied in the experimental setup (see Section 4.1). In contrast, the use of LLMs during the writing process for translation and polishing purposes does not require additional declaration, as the policy clearly states that such auxiliary uses do not affect the scientific rigor or originality the core methodology.
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Please refer our LLM policy should or should not be described.
for what
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Reviews:
[{"review_id": "jplWeewlSH", "reviewer": "Reviewer_6Pbn", "summary": "This 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.", "questions": "See weaknesses.", "limitations": "See weaknesses.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The authors partially solved my concerns. But there is no 0.5 points (for 4.5) in the system. I will keep my score at 4.", "rebuttal": "Dear Reviewer 6Pbn,\n\nWe sincerely appreciate the reviewer's insightful and constructive feedback, which highlights the strengths of our work and provides valuable guidance for future improvements. We have tried to address each of your concerns point by point in the rebuttal.\n\n**Q1: About the reliance on specific reasoning delimiters in SBT**\n\n**A1:**\nWe respectfully address the concern about generalizability limitations.   The reviewer's concern appears to center on the `<think>...  </think>` delimiter dependency, but this represents a misunderstanding of both our approach's flexibility and the current landscape of reasoning models.\n\n1.  **Current reasoning model landscape**: The vast majority of state-of-the-art reasoning models—including OpenAI o1, DeepSeek-R1, QwQ, Gemini 2.0 Flash Thinking, and Kimi-1.5—already employ explicit reasoning delimiters during inference.   This is not a limitation of our approach but rather an alignment with the dominant architectural paradigm in modern LRMs.\n\n2.  **Framework adaptability**: Our SBT framework is highly flexible regarding delimiter formats.   Adapting to different thinking markers is straightforward and easily implementable—we can simply modify the delimiter patterns in our parsing logic.   The core methodology—identifying overthinking patterns through efficiency metrics and linguistic markers—remains unchanged regardless of the specific delimiter format used.   For models without explicit delimiters, our framework can be adapted to identify reasoning segments through other structural cues or linguistic patterns.\n\nRather than representing a limitation, our focus on reasoning-capable models addresses the most practically relevant segment of current LRM deployment, where overthinking poses the greatest computational challenge.   The widespread adoption of structured reasoning in leading models suggests that delimiter-based approaches like ours are becoming increasingly relevant, not obsolete.\n\n**Q2: On Threshold Adaptability and Generalizability**\n\n**A2:**\nWe agree that the choice of predefined thresholds can influence the effectiveness of the SBT framework.      However, we would like to clarify that our method demonstrates robustness across a reasonably wide range of threshold values, and address the underlying concern about manual tuning requirements.\n\n**1. Empirical Evidence of Threshold Robustness**\n\nAs shown in Section 4.1, although we selected the best-performing threshold (0.2) to maximize performance, other threshold settings still achieved strong results.      Specifically:\n- **Threshold 0.3**: 44.0% average token reduction with 2.3% accuracy drop\n- **Threshold 0.4**: 43.0% average token reduction with 2.0% accuracy drop\n- Even under suboptimal thresholds, we maintain an average token reduction of 43.5%\n\nThis indicates that SBT does not require precise tuning to be effective and retains strong performance under relaxed configurations.      The performance degradation is gradual rather than cliff-like, suggesting inherent stability in our approach.\n\n**2. Cross-Model Generalization Without Retuning**\n\nMore importantly, our experiments demonstrate that **the same threshold (0.2) works effectively across diverse model architectures** without requiring model-specific adjustments:\n- **Mathematical specialists** (Qwen2.5-Math-1.5B/7B): 48.9% and 30.7% token reduction respectively\n- **General-purpose models** (Llama-3.2-1B and Llama-3.1-8B): 54.2% and 62.8% token reduction respectively\n\nThis cross-model consistency suggests that overthinking patterns exhibit structural similarities that transcend specific architectures, making our thresholds naturally transferable.\n\n**3. Task-Agnostic Performance**\n\nOur evaluation across four distinct mathematical reasoning benchmarks (GSM8K, MATH500, AIME, AMC23) with varying difficulty levels shows consistent improvements using identical thresholds.      The framework adapts organically to task complexity through the Overthink Score mechanism itself, rather than requiring manual threshold adjustments for each benchmark.\n\n**4. Theoretical Foundation for Threshold Selection**\n\nThe threshold range (0.2-0.4) corresponds to identifying 40-60% of reasoning instances as containing overthinking, which aligns with our structural analysis in Section 2.1 showing that Evolution Solutions (where overthinking primarily occurs) constitute a significant portion of reasoning trajectories.      This provides a principled basis for threshold selection rather than purely empirical tuning.\n\n**5. Comparison with Existing Approaches**\n\nWe note that most existing overthinking mitigation methods require significantly more complex parameter tuning:\n\n\n* **RL-based approaches** require reward function design, learning rates, and exploration parameters, and so on.[1-3]\n\n* **External constraint methods** need token budgets and stopping criteria calibration, and so on.[4,5]\n\n* **Dynamic inference methods** require confidence thresholds and early-stopping parameters, and so on.[6-8]\n\nIn contrast, our approach requires tuning of essentially one primary hyperparameter (τ₁), with the secondary threshold (τ₂) set as a simple offset (τ₁ + 5%).\n\nOur focus is not on chasing peak metrics through intensive hyperparameter search, but on developing a generalizable and stable framework that gracefully balances accuracy and efficiency across different models and tasks with minimal manual intervention.\n\n[1] Aggarwal, Pranjal, and Sean Welleck. \"L1: Controlling how long a reasoning model thinks with reinforcement learning.\" COLM 2025.\n\n[2] Luo, Haotian, et al. \"O1-pruner: Length-harmonizing fine-tuning for o1-like reasoning pruning.\" arXiv preprint arXiv:2501.12570, 2025.\n\n[3] Shen, Yi, et al. \"Dast: Difficulty-adaptive slow-thinking for large reasoning models.\" arXiv preprint arXiv:2503.04472, 2025.\n\n[4] Han, Tingxu, et al. \"Token-budget-aware llm reasoning.\" ACL 2025.\n\n[5] Xu, Silei, et al. \"Chain of draft: Thinking faster by writing less.\" arXiv preprint arXiv:2502.18600, 2025.\n\n[6] Yang, Chenxu, et al. \"Dynamic early exit in reasoning models.\" arXiv preprint arXiv:2504.15895, 2025.\n\n[7] Zhang, Jintian, et al. \"Lightthinker: Thinking step-by-step compression.\" arXiv preprint arXiv:2502.15589, 2025.\n\n[8] Manvi, Rohin, Anikait Singh, and Stefano Ermon. \"Adaptive inference-time compute: Llms can predict if they can do better, even mid-generation.\" arXiv preprint arXiv:2410.02725, 2024.\n\n**Q3: About the performance**\n\n**A3:**\nWe take the issue of premature termination seriously and have carefully designed our framework to mitigate it through the following three strategies:\n\n1.   **Adaptive Reasoning Preservation:**\nBoth SBT-E and SBT-D are designed to preserve the Foundation Solution and at least part of the Evolution Solution, ensuring the model retains essential reasoning and self-correction capabilities.   In particular, SBT-D employs a dynamic, step-wise overthinking score to determine problem-specific stopping points.   This allows more complex problems to retain longer reasoning chains when necessary, reducing the risk of premature termination.   Additionally, for trajectories not identified as overthinking, we preserve the full reasoning process to enhance the model’s capacity when handling genuinely complex tasks.\n\n2.   **Exposure via Masked Redundant Reasoning:**\nInstead of removing overthinking completely, we retain a small portion of redundant reasoning as masked content.   This allows the model to observe overthinking patterns without reinforcing them, helping it to learn the distinction between necessary elaboration and redundancy.\n\n3.   **Empirical Safeguards:**\nExtensive results across benchmarks (e.g., MATH500, AIME) show that Self-Braking Tuning maintains, and occasionally even improves, accuracy while significantly reducing token usage.   Notably, SBT-D on Llama-3.1-8B improves accuracy by +2.62% on MATH500 while cutting tokens by 59%, suggesting that the model benefits from eliminating unnecessary or distracting reasoning without compromising essential steps.\n\n**Q4: About the Overthink Marker**\n\n**A4:** Thank you for your comment.  We would like to address your concerns from the following aspects:\n\n1. **Ease of Identifying Overthink Markers**\nWhile specific overthink markers may vary between models, identifying these markers is straightforward.  By manually inspecting or using a language model to analyze just a few dozen reasoning examples, one can easily collect a set of frequently occurring indicator tokens.  This simple process allows adapting the marker set to different models without much effort.\n\n2. **Common Linguistic Features in Reasoning Models**\nAdditionally, recent studies[1-5] have highlighted that tokens like \"wait,\" \"perhaps,\" and \"alternatively\" frequently appear in reasoning models and are often indicative of overthinking.  This has already become a widespread linguistic feature of reasoning models.\n\nIn summary, while specific markers may vary, our methodology for detecting and mitigating overthinking is adaptable and can be generalized across different reasoning models.\n\n[1]\"Wait, We Don't Need to 'Wait'! Removing Thinking Tokens Improves Reasoning Efficiency.\", arXiv:2506.08343\n\n[2]\"Missing Premise Exacerbates Overthinking: Are Reasoning Models Overthinking?\", arXiv:2504.06514\n\n[3]\"Demystifying Long Chain-of-Thought Reasoning in LLMs.\", ICML 2025.\n\n[4]\"Retro-Search: Exploring Untaken Paths for Deeper and Efficient Reasoning.\", arXiv:2504.04383\n\n[5]\"LLMs Can Easily Learn to Reason from Demonstrations: Structure, Not Content, Is What Matters!\", arXiv:2502.07374"}, {"review_id": "Gs7PLRlwgo", "reviewer": "Reviewer_Bv4y", "summary": "This 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.", "questions": "1. 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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "I have read the author's rebuttal and the comments from other reviewers, and after a comprehensive evaluation, I have decided to maintain my original score.", "rebuttal": "Dear Reviewer Bv4y,\n\nWe thank you for your thorough review and constructive feedback. We sincerely appreciate your positive recognition of the novelty of our framework, the clarity of our analysis, and the overall quality of the writing. We have addressed each of your concerns point by point in the rebuttal.\n\n---\n\n**W1: About the performance under SBT**\n\n**A1:**\n**1. On the Trade-off Between Accuracy and Efficiency:**\nWe would like to clarify that the trade-off between reasoning efficiency and accuracy is not a limitation of our method per se, but rather an inherent challenge in complex multi-step reasoning. Reducing redundant steps can, in some cases, limit the model’s ability to recover answers through repetition or self-correction. This phenomenon is particularly visible in math-specialized models (e.g., Qwen2.5-Math), which rely heavily on deep token-level exploration. Similar trade-offs between accuracy and efficiency have been observed in other studies as well, such as in [1] and [2], further validating this inherent challenge in multi-step reasoning tasks.   \n\n**2. About the Accuracy:**\nWe recognize that maintaining strong performance is critical, and we adopt multiple strategies to mitigate potential drops. These include diverse supervision (SBT-Exact + SBT-Dynamic), explicit braking prompts, and balanced training between full and early-stopped traces. As a result, the overall performance drop is strictly controlled (≤ 2.86%), with an average gap of only **1.43%** across models—while reducing token usage by up to **60%**.\n\n**3. On General Models:**\nImportantly, SBT is not merely efficient—it also enhances accuracy in some settings. On general-purpose models such as LLaMA,, the SBT-D variant yields consistent gains in both performance and efficiency. This highlights the method’s broader applicability beyond math-heavy tasks, and suggests that SBT can serve as a general-purpose optimization strategy for step-wise reasoning.\n\n[1] \"CoT-Valve: Length-Compressible Chain-of-Thought Tuning.\" ACL 2025. \n\n[2] \"TokenSkip: Token Pruning for Vision-Language Models.\" ACL 2025.\n\n---\n\n**W2: Addressing Module-Level Ablation Studies**\n\n**A2:**\n\nWe'd like to explain our original experimental design and provide the requested module-level evaluation.\n\n**1. Original Experimental Motivation:**\n\nOur initial focus was on comparing different guidance mechanisms (natural language vs. special tokens) rather than evaluating the necessity of guidance signals themselves.    This decision was based on preliminary observations that models without explicit termination cues showed poor self-regulation behavior, making the comparison between guidance types more immediately relevant than the presence vs. absence comparison.\n\n**2. Complete Module Ablation Results:**\n\nWe have now conducted the comprehensive ablation study removing natural language guidance entirely:\n\n| Configuration | Accuracy | Tokens | Performance Impact |\n|---------------|----------|--------|--------------------|\n| **w/ Natural Language** | **56.66%** | **1682** | **Baseline** |\n| w/o Natural Language | 56.39% | 1801 | -0.27% acc, +7.1% tokens |\n\n**Key Findings from Complete Ablation:** The removal of natural language guidance results in both **reduced accuracy** (-0.27%) and **increased token consumption** (+7.1%), demonstrating that explicit metacognitive cues provide measurable benefits for both reasoning quality and efficiency.\n\n**3. Individual Module Contributions:**\n\nThis complete ablation clarifies each component's role:\n- **Natural Language Guidance**: +0.27% accuracy, -6.6% tokens (vs. no guidance)\n- **Natural Language vs. Special Tokens**: +0.05% accuracy, -6.4% tokens (from Table 9)\n\nThe results confirm that explicit termination signals are essential for effective self-regulation, with natural language providing the most efficient implementation of this mechanism.\n\n---\n\n**W3: On Hyperparameter Sensitivity and Practical Usability**\n\n**A3:**\n\nWe appreciate the reviewer's focus on practical deployment considerations.   Our analysis reveals that the perceived hyperparameter complexity is actually a strength of interpretable design:\n\n**1.      Interpretable and modular parameter design:**\nUnlike black-box optimization methods, our SBT framework introduces hyperparameters that are **explicitly interpretable and modular**.      Each parameter controls observable behaviors directly linked to model outputs—for example, the threshold corresponds to measurable overthinking patterns in reasoning trajectories.      This transparency enables practitioners to adjust parameters **intuitively and purposefully** rather than relying on blind hyperparameter search, making the framework more accessible than alternative approaches requiring complex reward engineering.\n\n**2.      Robust performance across parameter ranges:**\nOur threshold ablation experiments (Table 2) demonstrate that while performance fluctuates between different values, **variations are smooth and bounded**.      Even the worst-performing threshold achieves substantial efficiency gains—specifically, **41.5% token reduction** (1917 vs. 3278 tokens) while maintaining competitive accuracy.      This indicates our framework is **not fragile**: moderate deviations from the “optimal” threshold do not lead to catastrophic drops in performance, as long as the deviation is not too large.\n\n**3.      Adaptive trade-offs by design, not deficiency:**\nThe accuracy-token usage trade-off represents a deliberate design feature rather than a limitation.      SBT explicitly enables flexible early termination to avoid unnecessary overthinking.      This adaptability makes our approach **flexible rather than rigid**, allowing practitioners to optimize for their specific requirements without framework modifications.\n\n---\n\n**Q1: Evaluation on Long CoT-Capable Models**\n\n**A1:**\n\nWe appreciate the reviewer's suggestion to evaluate SBT on models with inherent long CoT capabilities.          This was in fact an aspect we considered early in our experimentation process.          We did conduct preliminary experiments on DeepSeek-R1-Distill-Qwen-7B to address this question, and the results provide important insights into our method's applicability:\n\n**1. Experimental Results on DeepSeek-R1-Distill-Qwen-7B:**\n\n| Model | Accuracy | Avg Tokens |\n|-------|----------|------------|\n| DeepSeek-R1-Distill-Qwen-7B (Baseline) | 73.35% | 6364 |\n| DeepSeek-R1-Distill-Qwen-7B-SBT-E | 70.14% | 5513 |\n\nWhile SBT achieves a 13.4% token reduction, the accuracy drop (3.21%) is more significant than observed with base models, suggesting limited effectiveness when applied to already long CoT-optimized models.\n\n**2. Data Scale Analysis:**\nThe modest performance gains can be attributed to a fundamental data scale imbalance.        DeepSeek-R1-Distill models have been trained on approximately **800K high-quality long CoT reasoning trajectories**, while our SBT-enhanced dataset contains only **92K examples**.        This significant disparity (8.7× difference) means that the pre-existing long CoT patterns learned from the larger dataset naturally dominate during fine-tuning.\n\nThe limited scale of our SBT dataset is insufficient to substantially alter the reasoning behaviors already established through       extensive training on much larger long CoT corpora.        This explains why the efficiency improvements are modest—the model's existing reasoning patterns, optimized through large-scale CoT training, are more resistant to modification by our smaller intervention dataset.\n\n**3. Method Positioning:**\nOur results indicate that SBT is most effective for base models that have not undergone extensive long CoT training, where our 92K examples can meaningfully shape reasoning behaviors.        For models already trained on large-scale CoT data, SBT would require proportionally larger datasets to achieve competitive performance.\n\nThis finding suggests an important design consideration: **SBT is optimally positioned as a primary training enhancement rather than a secondary refinement technique**.        Future work could explore scaling our data construction methodology to generate larger SBT datasets comparable to existing long CoT training corpora.\n\n---\n\n**Q2: On Illustrative Examples and Summary Statistics**\n\n**A2:**\nWe appreciate the reviewer's constructive suggestion for enhancing the clarity of our data construction framework.    We address both recommendations below:\n\n1.  **Illustrative Examples**\nWe have already provided illustrative examples of our framework's impact in **Figures 1 and 2**.  The consistency between our training datasets and the trained models' inference behavior generally reflects the effectiveness of our data construction strategy.  We will include more detailed illustrative examples in the appendix of the camera-ready version.\n\n2.  **Summary Statistics of Dataset Construction Impact**\nWhile we briefly mentioned the proportion of overthinking cases in Section 4.1, we provide more comprehensive statistics here to better illustrate our framework's data construction impact:\n\n| Overthink Score Threshold | SBT-E Cases (%) | SBT-D Cases (%) |\n|-------------------------------|---------------------|---------------------|\n| 0.2                          | 55,515 (60.3%)      | 57,545 (62.5%)      |\n| 0.3                          | 46,217 (50.2%)      | 46,865 (50.9%)      |\n| 0.4                          | 37,745 (41.0%)      | 36,943 (40.1%)      |\n\n**Total dataset size**: 92,064 examples\n\nThis comprehensive data transformation directly translates to the substantial efficiency gains (30-60% token reduction) demonstrated in our experimental results, confirming the effectiveness of our systematic approach to overthinking identification and mitigation."}, {"review_id": "nNk0CrMME2", "reviewer": "Reviewer_S6DA", "summary": "This 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.", "questions": "1. 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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "After reading the authors' response, I have no further questions. The current scores align with the quality of this paper, so I have decided to maintain it.", "rebuttal": "Dear Reviewer S6DA,\n\nWe thank you for your thoughtful review and valuable feedback. We appreciate your recognition of our framework’s motivation, the effectiveness of our results, and the contribution of a useful dataset. We have carefully addressed your comments in the rebuttal.\n\n**Q1: About the new terms**\n\n**A1**：\nWe appreciate your feedback on the complexity of our terminology. Our intention was to precisely distinguish between different reasoning concepts and analysis dimensions, but we understand this may have impacted readability. In future versions, we will strive to streamline naming, ensure stylistic consistency, and provide clearer, more intuitive explanations upon first use to improve overall accessibility.\n\n**Q2: About the evaluation scope**\n\n**A2:**\nThank you for the suggestion.  We understand the importance of evaluating beyond a single domain and offer the following clarification and supporting results:\n\n**1. Focus on the math domain:** Our main evaluation centers on math-related tasks because the SBT framework is trained entirely on math data.  Overthinking behaviors are especially prominent and easier to analyze in this setting, given the well-defined reasoning structure and objective correctness of math problems.  This makes math particularly suitable for developing and initially validating our approach.\n\n**2. Generalization beyond math:** We agree that overthinking is not confined to mathematics.  To test the broader applicability of our method, we additionally evaluate SBT on **two out-of-domain benchmarks**, **MMLU-Redux** and **GPQA-Diamond**, which cover a range of factual and commonsense reasoning challenges.  The results below show that our methods (SBT-E and SBT-D) achieve consistent **token savings** while maintaining competitive accuracy across diverse model backbones—**without any out-of-domain fine-tuning**:\n\n| Base Model| Method   | MMLU-Redux (acc / tokens) | GPQA-Diamond (acc / tokens) | Average (acc / tokens) |\n| - | -| - | -| ---------------------- |\n| Qwen2.5-Math-1.5B-Instruct | baseline | 45.84% / 2061             | 24.75% / 5485               | 35.30% / 3773          |\n|  | sbt-e    | 43.12% / 1403             | 25.06% / 3194               | 34.09% / 2299          |\n|  | sbt-d    | 43.28% / 1566             | 26.20% / 3002               | **34.74%** / **2284**          |\n| Qwen2.5-Math-7B-Instruct   | baseline | 67.04% / 3229             | 41.15% / 8892               | 54.10% / 6061          |\n|  | sbt-e    | 65.84% / 1927             | 40.40% / 6205               | 53.12% / **4066**          |\n|   | sbt-d    | 66.39% / 1998             | 41.29% / 6706               | **53.84%** / 4352          |\n| LLaMA3.2-1B-Instruct       | baseline | 35.62% / 1933             | 17.99% / 9321               | 26.81% / 5627          |\n|  | sbt-e    | 32.24% / 770              | 24.24% / 3516               | 28.24% / 2143          |\n|                            | sbt-d    | 33.12% / 725              | 23.48% / 3157               | **28.30%** / **1941**          |\n| LLaMA3.1-8B-Instruct       | baseline | 80.53% / 2481             | 37.31% / 8918               | 58.92% / 5699          |\n|                            | sbt-e    | 77.46% / 1646             | 36.30% / 5346               | 56.88% / **3496**          |\n|                            | sbt-d    | 77.74% / 1668             | 36.91% / 6717               | **57.33%** / 4192          |\n\nThese results confirm that our proposed overthinking-aware strategies transfer well to **non-mathematical reasoning tasks** and generalize across model scales and architectures.  **Specifically, we observe consistent token reductions ranging from 26% to 65% across all model-task combinations, with accuracy drops typically limited to 1-3%**.  Notably, **smaller models (LLaMA3.2-1B) show the largest efficiency gains (65% token reduction) and sometimes even improved accuracy**, while larger models achieve more moderate but still substantial savings (26-40%).  **The fact that math-trained models maintain competitive performance on diverse reasoning tasks without domain-specific retraining demonstrates the fundamental nature of our self-regulation mechanism**—suggesting that learning to recognize reasoning sufficiency is a transferable meta-cognitive skill applicable across reasoning domains.\n\n**Q3: About Framework Generalizability and Improvements**\n\n**A3:**\n\n**1. On Multiple Hyperparameters and Complexity** \n\nOur Self-Braking Tuning framework actually requires **fewer hyperparameters than existing overthinking mitigation approaches**.         While we use two primary thresholds (τ₁ and τ₂), the secondary threshold is simply set as τ₁ + 5%, reducing this to essentially **one core hyperparameter**.         In contrast:\n\n- **RL-based approaches** require reward function design, learning rates, exploration parameters, and length penalty coefficients [1-3]\n- **External constraint methods** need token budgets, stopping criteria, and confidence thresholds [4,5]\n- **Dynamic inference methods** require multiple confidence thresholds, early-stopping parameters, and uncertainty estimation hyperparameters [6-8]\n\nOur approach represents a significant **simplification** in the hyperparameter space while maintaining effectiveness.\n\n**2. On Clear Hyperparameter Selection Guidance**\n\nWe agree that guiding parameter selection is essential;       therefore, we dedicate ourselves to providing **systematic empirical guidance** for hyper-parameter choice:\n\n**(1) For threshold τ₁:** Our analysis in Section 4.1 demonstrates that **τ₁ = 0.2 consistently achieves optimal performance** across all model architectures and task difficulties\n\n**(2) For weighting parameter β:** We supplemented our experiments with additional validations that confirmed the choice of β = 0.1.\n\n| Method | β    | Accuracy | Tokens |\n|--------|------|----------|--------|\n| SBT-E  | 0.05 | 56.48%   | 1762   |\n|        | **0.1** | **57.83%** | **1673** |\n|        | 0.15 | 56.52%   | 1874   |\n|        | 0.2  | 55.86%   | 1809   |\n| SBT-D  | 0.05 | 56.24%   | **1678** |\n|        | **0.1** | **56.66%** | 1682   |\n|        | 0.15 | 56.21%   | 1784   |\n|        | 0.2  | 55.74%   | 1814   |\n\nThis systematic analysis shows **β = 0.1 consistently achieves the best accuracy-efficiency trade-off** for both SBT variants, providing clear selection guidance rather than arbitrary parameter choices.\n\n**3. On Robustness Demonstrated by Consistent Improvements in Table 2-5**\n\n**(1) Purpose of Comprehensive Ablation Studies:** These ablation studies serve a **critical purpose**: identifying near-optimal configurations that maintain our primary objective of **dramatic efficiency gains (30-60% token reduction)** while preserving accuracy.     Our extensive experiments systematically explore the parameter space across:\n\n- **Multiple threshold values** (0.2, 0.3, 0.4)\n- **Alternative configurations** (step-level vs. token-level, natural language vs. special tokens)\n- **Various model architectures** (Qwen2.5-Math, Llama-3.1, Llama-3.2)\n\nThis systematic exploration establishes **optimal operating points** for Self-Braking Tuning across diverse experimental conditions.\n\n**(2) Beyond Peak Performance: Prioritizing Universal Applicability:** \n\nHowever, our focus extends beyond simply chasing peak numerical performance.     Instead, we aim to develop a **generalizable and stable methodology** that gracefully balances performance and efficiency across diverse conditions.     The consistent patterns observed across all experiments—rather than dramatic variations—indicate that SBT achieves **reliable performance without requiring extensive retuning** for different models or tasks.     This stability is precisely what enables practical deployment and broader applicability.\n\n**(3) Evidence of Framework Robustness:** The reviewer's observation about \"marginal improvements\" in Tables 2-5 actually demonstrates our framework's **key strength: robustness**.     The **consistency of results** across:\n- **Multiple model architectures**: Qwen2.5-Math, Llama-3.1, Llama-3.2\n- **Varying task difficulties**: GSM8K to AIME\n- **Different experimental configurations**: All ablation combinations\n\nThis consistency confirms that our **core mechanism—teaching models autonomous reasoning regulation—is fundamentally sound and transferable**.     This **robustness and reliability** across diverse conditions validates that SBT represents a **mature, deployable solution** rather than a method overfitted to specific experimental settings.\n\n[1] Aggarwal, Pranjal, and Sean Welleck. \"L1: Controlling how long a reasoning model thinks with reinforcement learning.\" arXiv preprint arXiv:2025.\n\n[2] Luo, Haotian, et al. \"O1-pruner: Length-harmonizing fine-tuning for o1-like reasoning pruning.\" arXiv preprint arXiv:2501.12570, 2025.\n\n[3] Shen, Yi, et al. \"Dast: Difficulty-adaptive slow-thinking for large reasoning models.\" arXiv preprint arXiv:2503.04472, 2025.\n\n[4] Han, Tingxu, et al. \"Token-budget-aware llm reasoning.\" arXiv preprint arXiv:2412.18547, 2024.\n\n[5] Xu, Silei, et al. \"Chain of draft: Thinking faster by writing less.\" arXiv preprint arXiv:2502.18600, 2025.\n\n[6] Yang, Chenxu, et al. \"Dynamic early exit in reasoning models.\" arXiv preprint arXiv:2504.15895, 2025.\n\n[7] Zhang, Jintian, et al. \"Lightthinker: Thinking step-by-step compression.\" arXiv preprint arXiv:2502.15589, 2025.\n\n[8] Manvi, Rohin, Anikait Singh, and Stefano Ermon. \"Adaptive inference-time compute: Llms can predict if they can do better, even mid-generation.\" arXiv preprint arXiv:2410.02725, 2024.\n\n> Question: 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\nThank you for pointing this out. We must acknowledge that the lowest token cos for LLaMA-3.1-8B-Instruct was incorrectly marked due to an oversight on our part. We appreciate your careful reading and will correct this in the revised version."}, {"review_id": "bKMS9XQ48P", "reviewer": "Reviewer_FmRF", "summary": "This 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.", "questions": "Please refer to the weakness part above.", "limitations": "yes", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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", "was_revised": true, "final_justification": "I am still concerned about the methodology. I am concerned that the proposed method cannot truly achieve adaptive thinking because of the hard constraints on the constructed training data. I am staying negative.", "rebuttal": "Dear Reviewer FmRF,\n\nWe thank you for your thorough review and constructive feedback. We have tried to address each of your concerns point by point in the rebuttal.\n\n**Q1: About the Design of the Overthink Score**\n\n**A1**：\n\n**1. About the relation to prior ξ$_O$ metric [1]**:\nWe acknowledge that [1] is a valuable work, which we also cite.  However, our formulation introduces two key enhancements better suited for our Self-Braking Tuning framework:\n\n- **Purpose**: Unlike [1], we leverage RER not just for post-hoc analysis but as a core quantitative signal for data construction(Section 2.3), enabling step-wise adaptive truncation based on per-example overthink scores.\n\n- **Granularity**: Their ξ$_O$  operates at the token level, whereas ours works at the **reasoning step level**, respecting logical boundaries.   Our ablations confirm this structural granularity better supports overthinking mitigation (Section 4.3).\n\n**2. About the Reliability of OMR**\n\n**a. OMR as an intentionally sensitive signal:** We do not consider OMR unreliable, but rather intentionally sensitive to capture subtle linguistic cues of overthinking such as \"Wait\" or \"Alternatively.\"  Prior works [2,3] support using such markers to identify reasoning drift.\n\n**b. Balancing OMR's contribution:** To address potential noise, we assign OMR a low weight in the Overthink Score calculation, ensuring it informs without dominating the objective.\n\n**c. Ablation validation:** Removing OMR entirely shows its clear contribution:\n||Accuracy|Tokens|\n|-|-|-|\n|w/ OMR|57.83%|1673|\n|w/o OMR|56.25%|2071|\n\nOMR enhances both accuracy (+1.58%) and efficiency (-398 tokens) by capturing linguistic signals that RER alone misses, forming a complementary structural-linguistic detection mechanism.\n\n**3. On reasoning step segmentation:**\nReasoning steps are segmented by double newlines in model outputs—a widely validated approach in the literature [4,5]. Each segment separated by \\n\\n represents one reasoning step.\n\n[1] \"Do not think that much for 2+ 3=?  on the overthinking of o1-like llms.\"ICML 2025\n\n[2] \"Demystifying long chain-of-thought reasoning in LLMs.\"  ICLR 2025\n\n[3] \"Retro-search: Exploring untaken paths for deeper and efficient reasoning.\" arXiv:2504.04383\n\n[4] \"Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning.\"  ACL 2025\n\n[5] \"The lessons of developing process reward models in mathematical reasoning.\"  ACL 2025\n\n**Q2:About the Overthink Score**\n\n**A2:**\n\n**(1) On the Design Motivation for Setting β = 0.1 in the Overthink Score**\n\nWe set β = 0.1 to prioritize structural reliability (ηs) while preserving linguistic sensitivity (κt).  The 90:10 weighting ensures stability across different prompting styles while capturing subtle overthinking signals. Our ablation study validates this choice, showing consistent performance across threshold values.\n\n**(2) On the Empirical Justification for β = 0.1 through Ablation Study**\n\nTo support this design choice, we conducted an ablation study by varying β ∈ {0.05, 0.1, 0.15, 0.2} for both SBT-E and SBT-D truncation strategies. The results are shown below:\n\n|Method|β|Accuracy|Tokens|\n|-|-|-|-|\n|SBT-E|0.05|56.48%|1762|\n| |0.1|**57.83%**|**1673**|\n| |0.15|56.52%|1874|\n| |0.2|55.86%|1809|\n|SBT-D|0.05|56.24%|**1678**|\n| |0.1|**56.66%**|1682|\n| |0.15|56.21%|1784|\n| |0.2|55.74%|1814|\n\nThese results show β = 0.1 yields optimal accuracy-efficiency trade-offs across both methods. Higher β values cause accuracy degradation, confirming our parameter choice.\n\n**Q3: About the SBT data construction procedure**\n\n**A3**：\nWe would like to clarify that **SBT-Exact** and **SBT-Dynamic** are not final inference strategies, but **data construction methods** designed to guide the model toward learning when to stop reasoning.\n\n**(1) About the design of SBT-Exact:**\n\nWe address your concern through both existing results from Section 4.2 and additional experiments.  The systematic comparison across different solution counts demonstrates:\n\n|Solutions Retained|Accuracy|Tokens|\n|-|-|-|\n| 1 solution|56.95%|1700|\n| 2 solutions|**57.83%**|**1673**|\n| 3 solutions|57.81%|1771|\n\nThis analysis reveals that **retaining two complete solutions provides the optimal balance**: sufficient reasoning exposure for robust learning while establishing clear termination signals through answer convergence.\n\n**(2) About the design of SBT-Dynamic**:\nSBT-D uses τ₁=0.2 based on systematic evaluation across multiple values, identifying about 60% of samples as overthinking.  The step-wise evaluation enables **problem-specific adaptive termination**—simple problems stop after 3-4 steps while complex ones continue for 15-20 steps, determined by the model's own reasoning patterns rather than fixed cutoffs.\n\n**(3) About spontaneous overthinking control**:\n\nThese augmentations develop **model self-awareness of overthinking**. After SBT, generation becomes **fully autonomous**—models learn to shorten outputs and signal stopping without external control (Fig. 1&2). Analysis of **Qwen2.5-Math-7B-SBT-E** on AIME shows substantial outputs naturally terminate early via epiphany sentences, achieving both higher efficiency and accuracy.\n\n||Type|% of Cases|Acc|Tok|\n|-|-|-|-|-|\n|AIME24|Early Exit|50.83%|41.80%|5692|\n||No Early Exit|49.17%|38.98%|11084|\n|AIME25|Early Exit|49.17%|41.53%|6483|\n||No Early Exit|50.83%|32.79%|12201|\n\nThese findings further support that the self-braking behavior is not enforced by hard constraints, but emerges naturally during inference—yielding better performance with less computation.\n\n**Q4:About Performance Under SBT**\n\n**A4:**\nWe agree that model performance should remain the priority.    Our results reflect an inherent **accuracy-efficiency trade-off**, consistent with prior efficiency-focused work [1,2].\n\n1.  **Performance preservation strategies:** We actively mitigate performance degradation through several strategies:\n\n(1) **dual supervision** using SBT-Exact and SBT-Dynamic for both fixed and adaptive stopping patterns,\n\n(2) **explicit braking cues** providing natural language signals for sufficient reasoning depth.\n\n(3) **balanced training** mixing full and truncated trajectories to preserve reasoning depth.\n\nUnder these measures, we achieve well-controlled performance impact with an average accuracy drop of 1.43% across all models and maximum drop of 2.86%.  Notably, for general models (LLaMA), SBT-D actually improves both accuracy and efficiency, demonstrating the method's potential in broader settings.\n\n2.  **Key insight**: Baseline models may achieve correctness through redundancy rather than focused reasoning.    Our approach teaches models to reach correct answers more efficiently, with the slight accuracy trade-off reflecting elimination of this redundant computation rather than loss of reasoning capability.\n\n[1] Ma, Xinyin, et al. \"CoT-Valve: Length-Compressible Chain-of-Thought Tuning.\" ACL 2025\n\n[2] Xia, Heming, et al. \"TokenSkip: Token Pruning for Vision-Language Models.\" ACL 2025.\n\n**Q5: On the Purpose and Effectiveness of Natural Language Guidance (NLG)**\n\n**A5:**\n\n1.  **Essential Role of Explicit Stopping Signals:**\nWithout NLG, our training data would consist merely of variable-length reasoning trajectories with abrupt truncations.       This creates a fundamental learning gap: models would have no explicit supervision signal for recognizing when reasoning becomes sufficient, instead passively waiting for external `</think>` markers.  Our ablation study confirms this necessity:\n\n|Configuration|Accuracy|Tokens|\n|-|-|-|\n|w/ Natural Language|56.66%|1682|\n|w/o Natural Language|56.39%|1801|\n\n**Removing guidance reduces accuracy (-0.27%) and increases token consumption (+7.1%)**, demonstrating that explicit metacognitive cues are essential for self-regulation learning.\n\n2.  **Superior Efficiency Over Special Tokens:**\nWhile Table 5 shows comparable overall performance, detailed results in Table 9 reveal NLG's advantages on challenging tasks:\n- **AIME**: 3381 vs. 3647 tokens (-7.3%), 14.17% vs. 12.34% accuracy (+14.8%)\n- **Overall**: 1682 vs. 1797 tokens (-6.4% average)\n\n**Performance differences are most pronounced on complex reasoning**, where natural language's semantic richness provides clearer termination cues.\n\n3.  **Key Advantages of Natural Language Signals:**\n- **Semantic alignment** with pretraining makes them easier to learn than special tokens\n- **Self-awareness** enables internal reasoning state reflection\n- **Fluent integration** allows natural generation during autonomous inference\n\n**Q6: On the necessity and contribution of Masked Redundant Thinking (MRT)**\n**A6:**\nWe address your concern in two parts:\n\n1.  **Foundational Motivation and Supporting Work:**\nMRT builds on established practices: [1] uses masked incorrect reasoning for step-level self-correction, adopted in [2, 3].  These works demonstrate that masking redundant reasoning improves abstraction, robustness, and self-regulation—core objectives of Self-Braking Tuning.\n\n2.  **Empirical Evidence and Ablation Study:**\nSection 4.2 shows shorter masked spans improve reasoning efficiency.  An ablation removing MRT yields:\n\n||Accuracy|Tokens|\n|-|-|-|\n|w/ MRT|57.83%|1673|\n|w/o MRT|58.02%|2306|\n\nWhile removing MRT marginally increases accuracy (+0.19%), it significantly raises token consumption (+37.84%), confirming **MRT's essential role in efficiency gains without compromising effectiveness**.\n\n[1] \"S³cmath: Spontaneous step-level self-correction makes large language models better mathematical reasoners.\"  AAAI 2025\n\n[2] \"Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models.\"  ACL 2024\n\n[3] \"Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning.\"  ICML 2025\n\n**Q7: On Paper Formatting**\n\n**A7:**\nWe thank the reviewer for pointing out the formatting issues. We will address both concerns in the revised manuscript to ensure full compliance with the conference's guidelines."}]

Author rebuttal:

Meta-review: This paper introduces Self-Braking Tuning (SBT), a framework for reducing “overthinking” in large reasoning models. The approach combines a metric for detecting redundant reasoning, adaptive data construction (SBT-E and SBT-D), and natural language braking prompts. Experiments on math benchmarks and some out-of-domain tasks show large reductions in token usage (30–60%) with small accuracy changes, and in some cases even improvements.

Strengths: The paper tackles an important and timely problem with a clear and novel framework. The design is interpretable, easy to follow, and integrates detection, data, and training in a clean way. Efficiency gains are substantial and the work is likely to spark further research on reasoning regulation.

Weaknesses: The main concerns are about adaptivity: SBT-E fixes the number of solutions, and SBT-D relies on thresholds, which some reviewers felt was not truly adaptive. The method also depends on heuristic markers and empirical hyperparams, and performance on very complex tasks can drop slightly. Evaluation is still math-heavy, and gains from some components are modest.

Overall, I find the contribution meaningful despite these limitations. The authors provided additional experiments and clarified that adaptivity shows up in solution depth and complexity rather than solution count, which is a reasonable stance. While not the final word on the problem, this work makes a solid step forward and will be of interest to the community.
Decision: Accept (poster)