Title: Thoughts Are All Over the Place: On the Underthinking of Long Reasoning Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Reasoning

Long COT

o1
Abstract: Long reasoning models (LRMs) such as OpenAI's o1 and DeepSeek's R1 have demonstrated remarkable abilities in complex reasoning tasks by scaling test-time compute and exhibiting human-like deep thinking. However, we identify a phenomenon we term underthinking, where LRMs frequently switch between different reasoning thoughts without sufficiently exploring promising paths to reach a correct solution. This behavior leads to inadequate depth of reasoning and decreased performance, particularly on challenging mathematical problems. To systematically analyze this issue, we conduct experiments on three challenging test sets and two representative open-source LRMs, revealing that frequent thought switching correlates with incorrect responses. We introduce a novel metric to quantify underthinking by measuring token efficiency in incorrect answers. To address underthinking, we propose a decoding strategy with thought switching penalty (Tip)  that discourages premature transitions between thoughts, encouraging deeper exploration of each reasoning path. Experimental results demonstrate that our approach improves accuracy across challenging datasets without requiring model fine-tuning. Our findings contribute to understanding reasoning inefficiencies in LRMs and offer a practical solution to enhance their problem-solving capabilities. Our code is open-source and available at https://github.com/wangyuenlp/underthinking.

Thoughts Are All Over the Place: On the Underthinking of Long Reasoning Models

Abstract

Long reasoning models (LRMs) such as OpenAI's o1 and DeepSeek's R1 have demonstrated remarkable abilities in complex reasoning tasks by scaling testtime compute and exhibiting human-like deep thinking. However, we identify a phenomenon we term underthinking , where LRMs frequently switch between different reasoning thoughts without sufficiently exploring promising paths to reach a correct solution. This behavior leads to inadequate depth of reasoning and decreased performance, particularly on challenging mathematical problems. To systematically analyze this issue, we conduct experiments on three challenging test sets and two representative open-source LRMs, revealing that frequent thought switching correlates with incorrect responses. We introduce a novel metric to quantify underthinking by measuring token efficiency in incorrect answers. To address underthinking, we propose a decoding strategy with thought switching penalty (TIP) that discourages premature transitions between thoughts, encouraging deeper exploration of each reasoning path. Experimental results demonstrate that our approach improves accuracy across challenging datasets without requiring model fine-tuning.

Our findings contribute to understanding reasoning inefficiencies in LRMs and offer a practical solution to enhance their problem-solving capabilities. Our code is opensource and available at https://github.com/wangyuenlp/underthinking .

1 Introduction

Long reasoning models (LRMs) (OpenAI, 2024; DeepSeek, 2025; Qwen, 2024; Kimi, 2025) have revolutionized artificial intelligence by enabling models to tackle increasingly complex tasks. LRMs, known for their deep reasoning capabilities, exemplify the potential of large language models (LLMs) to exhibit human-like deep thinking by scaling test-time computation during problem-solving. These models aim to explore diverse reasoning strategies, reflect on their decisions, and iteratively refine solutions, closely mimicking human cognitive processes.

Despite their successes, a critical yet underexplored question remains: Are long reasoning models thinking deeply enough? This study provides an initial exploration of this problem. In this work, we investigate a phenomenon we term underthinking , which refers to the tendency of LRMs to prematurely abandon promising lines of reasoning, leading to inadequate depth of thought. To systematically analyze underthinking, we conduct experiments on three challenging test sets (e.g., MATH500, GPQA Diamond, and AIME2024) and two open-source LRMs with visible long chains of thought (e.g., QwQ-32B-Preview and DeepSeek-R1-671B). Through extensive analyses, we found that underthinking manifests in the following patterns: (1) it occurs more frequently on harder problems, (2) it leads to frequent switching between different thoughts without reaching a conclusion in each, and (3) it correlates with incorrect responses due to insufficient exploration of reasoning paths. For example, Figure 1 compares the token usage and number of thoughts of correct and incorrect responses. On average, LRMs consume 225% more tokens in incorrect responses than in correct ones due to 418% more frequent thought-switching behaviors.

To quantify this phenomenon, we introduce a novel underthinking metric that measures token efficiency in incorrect responses by evaluating the proportion of the response that contributes to reaching correct thoughts. Combining the widely-used accuracy metric with the proposed underthinking metric provides a more comprehensive assessment of LRMs models: accuracy measures how often the model can produce correct responses , while the underthinking metric evaluates the token efficiency within incorrect responses that contributes to reaching correct thoughts.

In response to these findings, we propose a decoding strategy with thought switching penalty (TIP) that discourages premature transitions between thoughts during the generation process. By adjusting decoding penalties for tokens associated with thought switching, the model is encouraged to thoroughly develop each line of reasoning before considering alternatives. Experimental results show that employing TIP improves accuracy across challenging test sets without requiring additional model fine-tuning.

Our study makes the following contributions:

We formally define and characterize the underthinking issue in long reasoning models, where models frequently abandon promising reasoning paths prematurely, leading to inadequate depth of reasoning on challenging problems.

We introduce a novel metric to evaluate underthinking by measuring token efficiency in incorrect responses, providing a quantitative framework to assess reasoning inefficiencies.

We propose a decoding approach with thought switching penalty (TIP) that encourages models to deeply explore each reasoning thought before switching, improving accuracy without additional model fine-tuning.

2 Observing Underthinking Issues

In this section, we present a comprehensive analysis of outputs from LRMs on challenging math problems . We begin by illustrating the frequent thinking switch phenomenon observed in responses to these problems, as shown in Figure 2, highlighting how this behavior differs significantly between correct and incorrect answers (Section 2.1). We then show that this phenomenon leads to an inadequate depth of reasoning, causing models to abandon promising reasoning paths prematurely (Section 2.2).

Solution

1

Let a, b, x, and y be real numbers with a>4 and b>1 such that \frac{x^2}{a^2}+\frac{y^2}{a^2-16}

=\frac{(x-20)^2}{b^2-1}+\frac{(y-11)^2}{b^2}=1. Find the least possible value of a+b.

So I've got this problem here that involves some equations with variables a, b, x, and y, and I need to find the least possible value of a

+ b given some conditions. ... Perhaps I can think of this as a system of equations and try to solve for x and y in terms of a and b.

Thought

1

411 tokens

Alternatively

, maybe there's a geometric interpretation. Since both equations represent ellipses, perhaps the condition is that these

ellipses are tangent to each other. ... So, unless there's some transformation or something, they don't seem to be the same ellipse.

Thought

2

588 tokens

Alternatively

, perhaps the problem is that for some (x,y), both equations hold simultaneously, and I need to find a and b such that this

is possible. ... solve the first equation for y in terms of x and substitute into the second equation. But that seems messy.

Thought

3

109 tokens

Alternatively

, perhaps I can consider this as an optimization problem where I need to minimize a + b subject to the condition that the

equations have a solution. ... But I'm not sure how to set that up algebraically.

Thought

4

280 tokens

Alternatively

, perhaps I can consider the distance between the centers of the two ellipses and relate it to their axes. ... But (y - 11)^2

is y^2 - 22y + 121. However, I only have y^2, not y. So maybe this isn't the best way to substitute.

Thought

5

613 tokens

……

Given that, perhaps I need to accept that this problem is beyond my current ability and seek help or look for a different problem.

**Final Answer**:   \[ \boxed{15} \]

Q:

20

More Thoughts

Averaged

284 tokens

Based on this observation, we propose a metric to empirically assess the underthinking issues and present empirical results in Section 2.3. We conclude that LRMs often underthink when they fail to tackle challenging math problems .

2.1 Frequent Thinking Switch of LRMs

We conduct experiments on three widely-used challenging testsets: MATH500 (Hendrycks et al., 2021), GPQA Diamond (Rein et al., 2023), and AIME 2022-2024 (MAA Committees). We mainly investigate two widely recognized open-source LRMs featuring visible long CoT: QwQ-32B-Preview and DeepSeek-R1-671B. We also include DeepSeek-R1-Preview to show the development of R1 series models. Given DeepSeek-R1-Preview's daily message limit of 50 via web interface, we evaluated this model solely on the MATH500 and AIME test sets.

Definition of Reasoning Thoughts In this paper, we define thoughts as the intermediate cognitive steps within a reasoning solution produced by the model. LRMs often switch reasoning thoughts using terms like 'alternatively'. For instance, as shown in Figure 2, the problem-solving process involves multiple reasoning thoughts, shifting from algebraic manipulation to geometric interpretation and optimization strategies. The ability to switch between different reasoning strategies allows for a broader exploration of potential solutions and demonstrates the flexibility of the model in tackling complex problems. In this study, we provide a comprehensive analysis of the side effects associated with this ability to switch reasoning thoughts.

We utilize the Llama-3.3-70B model to automatically segment a response into reasoning thoughts due to its superior capabilities in both instruction following and mathematical reasoning. Initially, we manually analyzed responses from the QwQ-32B-Preview model to gather expressions indicative of shifts in thought. We then tasked the Llama-3.3-70B model with scanning the entire response to identify all occurrences of such expressions. Furthermore, we asked the model to determine whether these expressions truly signify a change in thought or merely reflect a stylistic pattern in the response. Only the expressions indicating a genuine thought shift were used as separators for reasoning processes.

LRMs Switch Thinking More Frequently on Harder Problems Figure 3 shows the averaged thoughts and tokens in generated responses across various difficulty levels in the MATH500 test set. Clearly, all models generate more reasoning thoughts with the increase of difficulty level, which is

QwQ-32B-Preview

DS-R1-Preview

DS-R1-671B

QwQ-32B-Preview

DS-R1-Preview

DS-R1-671B

QwQ-32B-Preview

DS-R1-Preview

DS-R1-671B

0

1

2

3

4

5

#Thoughts

0

2

4

6

8

10

12

Level (Difficulty

↑

)

1

2

3

4

5

Thoughts

Tokens

4.0

2.3

1.9

1.5

1.1

11.2

6.2

4.8

4.2

3.5

11.2

6.2

4.8

4.2

3.5

0

1

2

3

4

5

#Thoughts

0

2

4

6

8

10

12

Level (Difficulty

↑

)

1

2

3

4

5

Thoughts

Tokens

3.3

2.1

1.5

1.2

1.0

9.4

5.6

2.9

2.7

3.1

9.4

5.6

2.9

2.7

3.1

0

1

2

3

4

5

#Thoughts

0

2

4

6

8

10

12

Level (Difficulty

↑

)

1

2

3

4

5

Thoughts

Tokens

4.4

2.8

2.1

1.4

1.4

6.0

4.6

3.3

2.0

2.6

6.0

4.6

3.3

2.0

2.6

Correctness Ratio

0%

10%

20%

30%

40%

50%

Index of Thought

1

2

3

4

5

6

7

8

9

10

32.0%

28.1%

32.4%

34.3%

28.6%

33.3%

30.6%

30.6%

30.6%

38.9%

25.5%

33.3%

39.4%

24.2%

44.1%

31.4%

48.6%

31.4%

37.1%

31.4%

19.0%

13.3%

13.3%

20.0%

13.3%

46.7%

20.0%

20.0%

25.0%

25.0%

DeepSeek-R1-671B

DeepSeek-R1-Preview

QwQ-32B-Preview

Distribution

0%

20%

40%

60%

80%

100%

Ratio of Correct Thoughts

>0

0.1

0.2

0.3

0.4

0.5

0.6

16.2%

24.3%

32.4%

48.6%

59.5%

67.6%

78.4%

13.2%

21.1%

36.8%

47.4%

63.2%

71.1%

84.2%

5.6%

5.6%

11.1%

22.2%

38.9%

55.6%

72.2%

(a) QwQ-32B-Preview

0

1

2

3

4

5

#Thoughts

0

2

4

6

8

10

12

Level (Difficulty

↑

)

1

2

3

4

5

Thoughts

Tokens

4.0

2.3

1.9

1.5

1.1

11.2

6.2

4.8

4.2

3.5

11.2

6.2

4.8

4.2

3.5

0

1

2

3

4

5

#Thoughts

0

2

4

6

8

10

12

Level (Difficulty

↑

)

1

2

3

4

5

Thoughts

Tokens

3.3

2.1

1.5

1.2

1.0

9.4

5.6

2.9

2.7

3.1

9.4

5.6

2.9

2.7

3.1

0

1

2

3

4

5

#Thoughts

0

2

4

6

8

10

12

Level (Difficulty

↑

)

1

2

3

4

5

Thoughts

Tokens

4.4

2.8

2.1

1.4

1.4

6.0

4.6

3.3

2.0

2.6

6.0

4.6

3.3

2.0

2.6

Correctness Ratio

0%

10%

20%

30%

40%

50%

Index of Thought

1

2

3

4

5

6

7

8

9

10

32.0%

28.1%

32.4%

34.3%

28.6%

33.3%

30.6%

30.6%

30.6%

38.9%

25.5%

33.3%

39.4%

24.2%

44.1%

31.4%

48.6%

31.4%

37.1%

31.4%

19.0%

13.3%

13.3%

20.0%

13.3%

46.7%

20.0%

20.0%

25.0%

25.0%

DeepSeek-R1-671B

DeepSeek-R1-Preview

QwQ-32B-Preview

Distribution

0%

20%

40%

60%

80%

100%

Ratio of Correct Thoughts

>0

0.1

0.2

0.3

0.4

0.5

0.6

16.2%

24.3%

32.4%

48.6%

59.5%

67.6%

78.4%

13.2%

21.1%

36.8%

47.4%

63.2%

71.1%

84.2%

5.6%

5.6%

11.1%

22.2%

38.9%

55.6%

72.2%

(b) DeepSeek-R1-Preview

0

1

2

3

4

5

#Thoughts

0

2

4

6

8

10

12

Level (Difficulty

↑

)

1

2

3

4

5

Thoughts

Tokens

4.0

2.3

1.9

1.5

1.1

11.2

6.2

4.8

4.2

3.5

11.2

6.2

4.8

4.2

3.5

0

1

2

3

4

5

#Thoughts

0

2

4

6

8

10

12

Level (Difficulty

↑

)

1

2

3

4

5

Thoughts

Tokens

3.3

2.1

1.5

1.2

1.0

9.4

5.6

2.9

2.7

3.1

9.4

5.6

2.9

2.7

3.1

0

1

2

3

4

5

#Thoughts

0

2

4

6

8

10

12

Level (Difficulty

↑

)

1

2

3

4

5

Thoughts

Tokens

4.4

2.8

2.1

1.4

1.4

6.0

4.6

3.3

2.0

2.6

6.0

4.6

3.3

2.0

2.6

Correctness Ratio

0%

10%

20%

30%

40%

50%

Index of Thought

1

2

3

4

5

6

7

8

9

10

32.0%

28.1%

32.4%

34.3%

28.6%

33.3%

30.6%

30.6%

30.6%

38.9%

25.5%

33.3%

39.4%

24.2%

44.1%

31.4%

48.6%

31.4%

37.1%

31.4%

19.0%

13.3%

13.3%

20.0%

13.3%

46.7%

20.0%

20.0%

25.0%

25.0%

DeepSeek-R1-671B

DeepSeek-R1-Preview

QwQ-32B-Preview

Distribution

0%

20%

40%

60%

80%

100%

Ratio of Correct Thoughts

>0

0.1

0.2

0.3

0.4

0.5

0.6

16.2%

24.3%

32.4%

48.6%

59.5%

67.6%

78.4%

13.2%

21.1%

36.8%

47.4%

63.2%

71.1%

84.2%

5.6%

5.6%

11.1%

22.2%

38.9%

55.6%

72.2%

(c) DeepSeek-R1-671B

0

2000

4000

6000

8000

10000

12000

14000

QwQ-Prev

R1-Prev

R1-671B

7,271

6,843

9,769

3,706

2,624

2,883

Correct Response

Incorrect Response

Number of Thoughts

0

10

20

30

40

QwQ-Prev

R1-Prev

R1-671B

25.2

23.8

36.3

4.5

6.6

6.6

0

2000

4000

6000

8000

QwQ-Prev

R1-671B

7,271

3,439

4,441

3,086

Correct Response

Incorrect Response

Number of Thoughts

0

5

10

15

QwQ-Prev

R1-671B

10.6

12.9

5.1

9.9

(a) Math500-Hard

0

2000

4000

6000

8000

10000

12000

14000

QwQ-Prev

R1-Prev

R1-671B

7,271

6,843

9,769

3,706

2,624

2,883

Correct Response

Incorrect Response

Number of Thoughts

0

10

20

30

40

QwQ-Prev

R1-Prev

R1-671B

25.2

23.8

36.3

4.5

6.6

6.6

0

2000

4000

6000

8000

QwQ-Prev

R1-671B

7,271

3,439

4,441

3,086

Correct Response

Incorrect Response

Number of Thoughts

0

5

10

15

QwQ-Prev

R1-671B

10.6

12.9

5.1

9.9

(b) GPQA Diamond

10.8%

5.6%

10.8%

5.6%

10.8%

5.6%

0.7

0.7

0.7

consistent with the growth of generated tokens. This observation suggests that as the complexity of the problems increases, the models tend to switch thoughts more frequently. This behavior implies that LRMs are able to dynamically adjust their reasoning processes to tackle more challenging problems. The following experiments focus on Level 5 in the MATH500 test set (MATH500-Hard).

Increased Thought Switching in LRMs during Incorrect Responses When examining the behavior of LRMs, we observe a distinct pattern in how they handle incorrect responses. As depicted in Figures 1 and 4, these models exhibit a significant increase in the frequency of thought switching while generating incorrect answers across all test sets. This trend suggests that although the models are designed to dynamically adjust their cognitive processes to solve problems, more frequent thought switching does not necessarily lead to higher accuracy. Essentially, the models may be expending additional computational resources - evidenced by an increase in generated tokens - without achieving more accurate solutions. These insights are crucial because they highlight the need not only to explore additional cognitive pathways when faced with challenges but also to operate in a more targeted and efficient manner , thereby improving accuracy even when complex reasoning is required. In the following sections, we empirically validate the inefficiencies associated with frequent thought switching in incorrect responses.

2.2 Existence of Underthinking

The behavior of frequent thinking switch in incorrect responses could stem either from (1) genuine underthinking, where the model succeeds in finding promising strategies but fails to stick with them, or from (2) a lack of understanding, prompting it to explore diverse but ineffective approaches. To disentangle these possibilities, we propose an assessment framework that evaluates whether an abandoned reasoning path is actually sufficient to derive a correct answer. By focusing on whether the model can persistently follow and deepen a single, promising line of thought, we can identify instances of underthinking.

Assessing Thought Correctness In the example presented in Figure 2, we observe that some early thoughts may lead to the correct answer. For instance, Thought 1 initiates a correct interpretation

DeepSeek-R1-671B

DeepSeek-R1-671B

DeepSeek-R1-671B

DeepSeek-R1-Preview

DeepSeek-R1-Preview

DeepSeek-R1-Preview

QwQ-32B-Preview

QwQ-32B-Preview

QwQ-32B-Preview

10.5%

10.5%

10.5%

5.3%

5.3%

5.3%

10.8%

10.8%

10.8%

0.0%

0.0%

0.0%

0.8

0.8

0.8

0.0%

0.0%

0.0%

2.7%

2.7%

2.7%

0.0%

0.0%

0.0%

0.9

0.9

0.9

#Thoughts

12

10

8

6

4

2

0

Correctness Ratio

3.7

3.7

1.1

1

50%

40%

31.6%

26.7%

30%

20%

21.9%

10%

0%

1

1.5

by recognizing that the given equations resemble those of ellipses centered at (0,0) and (20,11). Setting the two expressions equal is a valid approach to finding common points ( x, y ) that satisfy both equations. Instead of concentrating on thoroughly exploring the plausible thought with further algebraic manipulation and optimization techniques, the model frequently shifts its focus and uses approximately 7,270 additional tokens without arriving at a correct answer. Ultimately, it concludes with a guessed answer that lacks support from the extended COT process.

We leverage LLMs to assess whether each thought leads to a correct answer using the prompt detailed in Appendix A.Specifically, we use two models distilled from DeepSeek-R1-671B based on Llama and Qwen -DeepSeek-R1-Distill-Llama-70B and DeepSeek-R1-Distill-Qwen-32B , which achieve new state-of-the-art results for dense models across various reasoning benchmarks. If at least one model generates a confidence score of 2 for a thought, we regard it as a correct thought. QwQ-32B-Preview 4 5 10 12 Thoughts Tokens 11.2 11.2 DS-R1-Preview 10 12 Thoughts Tokens

↑

We evaluate the accuracy of our assessment approach using responses generated by Qwen-32BPreview for 90 instances from the AIME 22-24 test sets. We utilize the final thought in each response as the test example and its correctness as the ground-truth label. To ensure a fair comparison, we randomly streamline correct thoughts to match the average length of incorrect thoughts. Ultimately, we have 35 correct thoughts with an average length of 278.1 tokens and 55 incorrect thoughts with an average length of 278.3 tokens. Our assessment approach achieves accuracies of 82.9% for correct examples and 81.8% for incorrect examples, demonstrating its effectiveness. #Tokens (K) 0 1 2 3 #Thoughts 0 2 4 6 8 Level (Difficulty ↑ ) 1 2 3 4 5 4.0 2.3 1.9 1.5 1.1 6.2 4.8 4.2 3.5 6.2 4.8 4.2 3.5 #Thoughts 0 2 4 6 8 Level (Difficulty 1 2 3 4 2.1 1.5 1.2 1.0 5.6 2.9 2.7 3.1 5.6 2.9 2.7 3.1

Early-Stage Thoughts Are Correct but Abandoned in Incorrect Responses Figure 5 depicts the ratio of correct thoughts at each index in incorrect responses on the three challenging test sets. The analysis highlights a critical insight into the phenomenon of underthinking. Specifically, a notable proportion of initial thoughts across various models were correct but were not pursued to completion. This tendency to abruptly shift away from these promising thoughts indicates an inadequate depth of reasoning, where potentially correct solutions are prematurely abandoned before being thoroughly explored. This observation suggests a need for enhancing the QwQ-32B-Preview #Tokens (K) 1 2 3 4 5 Thoughts Tokens 4.0 2.3 11.2 6.6 4.6 4.0 11.2 6.6 4.6 4.0 DS-R1-Preview #Thoughts 2 4 6 8 10 12 Thoughts Tokens 3.5 2.3 1.6 1.2 1.1 9.4 5.6 2.9 3.0 3.1 9.4 5.6 2.9 3.0 3.1

2

1.9

3

Level (Difficulty

1.5

5

4

3

2

1

models' ability to persistently explore a specific line of reasoning deeply and accurately before opting to switch to alternative thought processes. 0 ↑ ) 4 5 0 0 Level (Difficulty ↑ ) 1 2 3 4 5 0 0 Level (Difficulty ↑ ) 1 2 3 4 5

47.1% DeepSeek-R1-671B Most Incorrect Responses Contain Correct

Index of Thought 3 4 5 6 7 8 9 ≥ 10 24.9% 25.9% 25.9% 21.4% 17.9% 36.7% 26.7% 30.0% 13.6% 19.4% 22.6% 18.8% 12.5% 25.0% 18.8% 18.8% 18.3% 17.6% 23.5% 29.4% 17.6% 23.5% 17.6% DeepSeek-R1-Preview QwQ-32B-Preview Thoughts Figure 6 plots the thought correctness ratios in incorrect responses from various models. We observe that over 70% of incorrect responses contain at least one correct thought. Furthermore, in more than 50% of these responses, over 10% of the thoughts are correct. Combined with observations from Figure 5, this suggests that while LRMs can initiate correct reasoning pathways, they may struggle to continue these pathways to reach the correct conclusion. This highlights the importance of encouraging models to maintain and expand their initial correct thoughts to synthesize them into accurate final answers.

4.7

Correctness Ratio

0%

10%

20%

30%

40%

50%

Index of Thought

1

2

3

4

5

6

7

8

9

10

32.0%

28.1%

32.4%

34.3%

28.6%

33.3%

30.6%

30.6%

30.6%

38.9%

25.5%

33.3%

39.4%

24.2%

44.1%

31.4%

48.6%

31.4%

37.1%

31.4%

19.0%

13.3%

13.3%

20.0%

13.3%

46.7%

20.0%

20.0%

25.0%

25.0%

DeepSeek-R1-671B

DeepSeek-R1-Preview

QwQ-32B-Preview

DS-R1-671B

3

4

5

8

10

12

Thoughts

Tokens

6.1

6.1

4.6

4.6

3.0

2.3

1.5

Distribution

0%

20%

40%

60%

80%

100%

Ratio of Correct Thoughts

>0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

2.7%

10.8%

10.8%

16.2%

24.3%

32.4%

48.6%

59.5%

67.6%

78.4%

0.0%

5.3%

10.5%

13.2%

21.1%

36.8%

47.4%

63.2%

71.1%

84.2%

0.0%

0.0%

5.6%

5.6%

5.6%

11.1%

22.2%

38.9%

55.6%

72.2%

DeepSeek-R1-671B

DeepSeek-R1-Preview

QwQ-32B-Preview

These insights lead us to propose an underthinking metric based on the presence of the first correct thought in the subsequent section.

36.8%

30.0%

18.8%

2

6

)

9.4

9.4

3.3

5

5

4

3

2

1

0

Distribution

#Thoughts

100%

84.2%

80%

78.4%

72.2%

60%

40%

20%

0%

>0

1

1

2.3 Empirical Underthinking Results

In this section, we propose a metric for empirically assessing underthinking issues based on token efficiency, complementing the widely used accuracy metric.

Underthinking Metric Intuitively, if a model generates a correct thought at an early stage and then switches to other thoughts without reaching a correct answer, the tokens generated thereafter do not contribute to reaching a correct solution and are considered inefficient due to underthinking. The underthinking score, denoted as ξ UT , is defined as:

Here, N represents the number of instances in a given test set where the evaluated model generates incorrect responses. T i is the total number of tokens in the i -th incorrect response, and ˆ T i is the number of tokens from the beginning of that response up to and including the first correct thought. If there is no correct thought in the i -th response, ˆ T i = T i , indicating that the model lacks an understanding of this problem, leading it to explore diverse but ineffective approaches. Therefore, it cannot be considered underthinking. Consider Figure 2 as an example: the first reasoning thought can reach a correct answer if fully explored, with ˆ T = 411 . Consequently, ξ UT = 1 -411 7681 = 0 . 946 , which can be considered extremely inefficient, reflecting a high underthinking score.

The metric ξ UT complements the accuracy metric by quantifying the extent of underthinking by measuring the token efficiency in generating effective content within an incorrect response :

A lower value of ξ UT indicates higher token efficiency, meaning that a greater proportion of tokens in incorrect responses contribute towards reaching a correct thought before switching to another thought. This suggests that the model is more efficient in its token utilization even when it fails to provide a correct answer.

A higher value of ξ UT signifies lower token efficiency, indicating that a larger number of tokens do not contribute effectively towards generating a correct thought. This reflects greater underthinking, where the model generates redundant or irrelevant tokens by frequently switching thoughts.

Empirical Results Table 1 provides insights into model performance across challenging test sets, evaluating both accuracy and underthinking (UT) scores. Clearly, all LRMs suffer from significant underthinking issues, although there are considerable differences across models and test sets. The results reveals that the relationship between model accuracy and underthinking varies across different datasets. On the MATH500-Hard and GPQA Diamond datasets, higher accuracy achieved by the superior DeepSeekR1-671B model is accompanied by higher UT Scores, indicating more underthinking in incorrect responses.

This suggests that while the model is more capable overall, it may produce longer but less effective reasoning when uncertain, possibly due to exploring multiple incorrect reasoning paths without efficiently converging on the correct solution. Conversely, on the AIME2024 test set, the DeepSeekR1-671B model not only attains higher accuracy but also exhibits a lower UT score, reflecting less underthinking and greater token efficiency. This implies that the model's reasoning remains focused and effective even when it does not arrive at the correct answer, perhaps due to better alignment with the problem types and reasoning processes required by the AIME2024 task.

These findings illustrate that underthinking behavior is sensitive to the nature of the dataset and the tasks involved. The larger model's superior capabilities do not uniformly translate to less

underthinking across all tasks. In some cases, increased model capacity leads to more elaborate but inefficient reasoning in incorrect responses, while in others, it enhances both accuracy and reasoning efficiency. Understanding the underthinking phenomenon is crucial for developing models that not only provide correct answers but also exhibit effective reasoning processes.

Furthermore, due to different training strategies, models across different families may differ fundamentally: some reach correctness intuitively (low UT score), while others do so through iterative refinement (higher UT score). Since both approaches can achieve high accuracy, this observation explains the weak correlation between UT score and accuracy shown in Table 1. Nevertheless, within specific model families, UT scores show a clearer correlation with accuracy. This trend may result from similar training strategies that reduce behavioral variance, as supported by the results in Table 2. Within the Qwen3 family, for instance, we observed a clear trend where the severity of underthinking decreases as model size increases.

3 Mitigating Underthinking Issues

In this section, we propose a lightweight mechanism that mitigates underthinking issues without requiring any model fine-tuning.

3.1 Decoding with Thought Switching Penalty

Aforementioned findings show that LRMs prioritize exploring many solutions over deeply investigating one. Inspired by the success of the coverage penalty in neural machine translation (Tu et al., 2016; Wu et al., 2016), we propose a novel decoding algorithm with a thought switching penalty to encourage the model to explore potential thoughts more thoroughly before moving on to new ones.

Standard Decoding In standard decoding, the probability of each token v at position t is computed using the softmax function over the logits z t ∈ R | V | ( | V | is the vocabulary size) in the output layer:

where z t,v ∈ z t is the logit for token v . By repeating this step for each position in the sequence, the model generates sequences of tokens, computing probabilities for each possible continuation.

Thought Switching Penalty (TIP) To encourage the model to delve deeper into current thoughts before switching, we introduce a penalty on tokens that are associated with thought transitions. A key consideration in designing this penalty is to distinguish between unproductive, rapid thoughtswitching and deliberate, strategic shifts in reasoning (e.g., backtracking) (Gandhi et al., 2025). Therefore, TIP is designed to be selective: it specifically targets and penalizes thought-switching tokens only when they appear with high frequency within a recent context, a pattern indicative of shallow exploration. This ensures that isolated or intentional shifts in thought remain unpenalized, preserving the model's ability to employ effective complex reasoning strategies like backward chaining. Let ̂ V ⊂ V be the set of tokens associated with thought switching (e.g., 'alternatively'). We modify the logits as follows:

where

α ≥ 0 ( Penalty Strength ) is a parameter controlling the strength of the penalty applied to thoughtswitching tokens. A larger α results in a greater reduction of the logits for these tokens, making them less likely to be chosen.

β ≥ 0 ( Penalty Duration ) specifies the number of positions from the start of a thought at Ψ , during which the penalty is active. A larger β extends the penalty over more positions, further discouraging early thought switching.

When α = 0 or β = 0 , the penalty is effectively disabled, and the decoding process reduces to the standard decoding algorithm. The adjusted logits ˆ z t,v reduce the probability of generating thought-switching tokens within a specified window, encouraging the model to continue expanding on the current thought before moving on. The new probability distribution becomes:

3.2 Experimental Results

For reliable evaluation, we report Pass@1 computed from 32 samples per problem with a temperature of 0.7 and a top_p value of 0.95. We tuned α ∈ { 3 , 5 , 10 , 20 , 30 } and β ∈ { 300 , 400 , 500 , 600 , 700 } on the AIME 2022-2023 development set using QwQ-32B-Preview, selecting the best pair α = 3 , β = 600 for all models and benchmarks. Please refer Appendix B.2 for more details.

Standard Decoding Table 3 shows that our TIP method consistently improves performance in all cases by mitigating the underthinking issues. On AIME-24, Pass@1 improves by 5.8% on QwQ32B-Preview, 2.7% on R1-Distill-Qwen, and 1.0% on DeepSeek-R1, while the underthinking (UT) score drops across the board. Observing the indicators, we see fewer thought-switching tokens and larger intervals when using TIP, confirming that our method encourages models to explore individual reasoning paths more thoroughly and mitigates underthinking. For example, when applying TIP to DeepSeek-R1 on AIME2024, the average thought-switching tokens decreased (13.8 → 5.7), and the average interval between switches increased (580.1 → 941.6). These changes reflect fewer premature transitions, resulting in a more focused and human-like exploration of reasoning paths.

Best-of-N Sampling To further assess TIP, we combined it with two widely used best-of-N sampling methods: (1) Self-Consistency (Wang et al., 2023), selecting the most consistent answer from multiple samples; (2) Laconic Decoding (Raoof & Dimakis, 2025), selecting the shortest of multiple generated answers, based on the observation that correct responses often have fewer tokens.

Table 4 indicates that using TIP consistently boosts accuracy across all model-method combinations. For instance, applying TIP to Self-Consistency with QwQ-32B-Preview (4-sample setting) significantly raises accuracy (43.7% → 51.4%) and reduces UT scores (35.4 → 26.6). Similarly, combining Laconic Decoding with TIP yields consistent gains, particularly pronounced for stronger models (e.g., R1-Distill-Qwen: 74.4% → 78.0% at 8 samples). These results clearly demonstrate that the TIP method successfully complements existing sampling strategies, encouraging more thorough reasoning and reliably mitigating underthinking issues in challenging mathematical reasoning scenarios.

Overall, the TIP approach represents a significant step toward addressing the underthinking problem in LRMs. Although the hyperparameters are tuned on the AIME 2022 and 2023 test sets using the QwQ-32B-Preview model, the consistent improvements observed across various test sets and models with the same hyperparameters validate the generalizability of the method. Our findings also suggest that the TIP method synergizes well with best-of-N sampling strategies, leading to further improvements in accuracy and reductions in underthinking scores. This indicates that encouraging more thorough exploration of individual reasoning paths complements the diversity introduced by sampling methods.

4 Related Work

Scaling Test-Time Compute Recent advancements in deep reasoning models, such as OpenAI's o1, have emphasized scaling test-time compute to improve complex problem-solving capabilities. One approach focuses on expanding the search space by considering multiple candidate solutions during decoding, exemplified by self-consistency (Wang et al., 2023), best-of-n decoding, and minimum Bayes risk decoding (Lightman et al., 2024; Li et al., 2023; Khanov et al., 2024; Heineman et al., 2024; Wu et al., 2024). Another influential direction involves promoting human-like deep thinking , beginning with Chain-of-Thought (Wei et al., 2022), where models mimic human reasoning

processes (Cesista, 2024; Pfau et al., 2024). Recent models, such as QwQ (Qwen, 2024), DeepSeekR1 (DeepSeek, 2025), and Kimi-1.5 (Kimi, 2025), leverage reinforcement learning (RL) to enable strategic, reflective reasoning and improve accuracy in complex tasks.

Efficient Thinking Efficient reasoning matters as much for language reasoning models (LRMs) as it does for human cognition. Models sometimes suffer overthinking , wasting resources on trivial paths (Chen et al., 2024). In contrast, we focus on the less-explored issue of underthinking , where models prematurely abandon promising reasoning directions, limiting performance on challenging problems. Along this direction, recent research has begun exploring methods to enhance reasoning efficiency. For instance, Laconic decoding implements shortest-of-n decoding strategies to minimize error-prone lengthy responses (Raoof & Dimakis, 2025), while Muennighoff et al. (2025) propose techniques to optimize test-time computation through dynamic termination signals.

Manipulating Decoding Penalties Penalty mechanisms in NLP decoding have become increasingly relevant to addressing shortcomings in traditional search methods. Length normalization penalties (Jean et al., 2015; Koehn & Knowles, 2017; Tu et al., 2017; Murray & Chiang, 2018) encourage appropriate translation lengths, improving fluency and adequacy. Additionally, coverage penalties (Tu et al., 2016; Wu et al., 2016) reduce textual redundancies by ensuring comprehensive attention over source tokens. See et al. (2017) also applied these ideas to summarization tasks. In this work, we introduce specific decoding penalties to mitigate underthinking, prompting models to sustain deeper reflection rather than superficial or frequent strategy shifts. To our knowledge, we are the first to investigate decoding penalties to address the underthinking problem.

5 Conclusion

In this work, we identified underthinking as a key limitation for LRMs performing challenging reasoning tasks, highlighting how premature abandonment of promising paths reduces efficiency and accuracy. We developed a new metric to quantify underthinking based on token efficiency in incorrect responses, and introduced a decoding strategy - thought switching penalty (TIP) - to encourage deeper exploration before shifting reasoning directions. Empirical evaluations show that TIP significantly reduces underthinking and improves performance on difficult reasoning benchmarks, without requiring additional model training.

This work contributes to a deeper understanding of reasoning processes in LRMs and provides a practical approach to align their problem-solving capabilities. Future directions include exploring adaptive mechanisms within models to self-regulate thought transitions and further improving reasoning efficiency.

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A Assessing Thought Correctness

In the example presented in Figure 2, we observe that some early thoughts may lead to the correct answer. For instance, Thought 1 initiates a correct interpretation by recognizing that the given equations resemble those of ellipses centered at (0,0) and (20,11). Setting the two expressions equal is a valid approach to finding common points ( x, y ) that satisfy both equations. Instead of concentrating on thoroughly exploring the plausible thought with further algebraic manipulation and optimization techniques, the model frequently shifts its focus and uses approximately 7,270 additional tokens without arriving at a correct answer. Ultimately, it concludes with a guessed answer that lacks support from the extended COT process.

We leverage LLMs to assess whether each thought leads to a correct answer using the following prompt:

Problem P = {problem} Solution Draft S = {split solutions} Correct Answer A = {expected answer}

1. Please analyze the relevance between the solution S and the problem P, and conduct some verifications to check the correctness of the solution itself. Please think step by step to give an explanation **EXPLANATION**.

2. If you think the solution draft S can lead to the correct answer A of the problem P , please stick to the line of thinking without deviation and carry it through to completion. If you think it cannot yield the correct answer or you're not sure, don't force yourself to give an answer and generate **None**.

3. Please tell me honestly how confident you are that you can solve the problem P correctly based on the the solution draft S. Out of 2, please generate your confidence score **CONFI-DENT_SCORE**.

Please output **EXPLANATION** and **CONFIDENT_SCORE** according to the following format:

EXPLANATION: \boxed{}

CONFIDENT_SCORE: \boxed{}

Specifically, we use two models distilled from DeepSeek-R1-671B based on Llama and Qwen -DeepSeek-R1-Distill-Llama-70B and DeepSeek-R1-Distill-Qwen-32B , which achieve new state-ofthe-art results for dense models across various reasoning benchmarks. If at least one model generates a confidence score of 2 for a thought, we regard it as a correct thought.

We evaluate the accuracy of our assessment approach using responses generated by Qwen-32BPreview for 90 instances from the AIME 2022, 2023, and 2024 test sets. We utilize the final thought in each response as the test example and its correctness as the ground-truth label. To ensure a fair comparison, we randomly streamline correct thoughts to match the average length of incorrect thoughts. Ultimately, we have 35 correct thoughts with an average length of 278.1 tokens and 55 incorrect thoughts with an average length of 278.3 tokens. Our assessment approach achieves accuracies of 82.9% for correct examples and 81.8% for incorrect examples, demonstrating its effectiveness.

B Experimental Details of TIP

B.1 Selection of Thought-Switching Tokens

A key component of our TIP decoding strategy is the use of a predefined set of thought-switching tokens that signal a potential switch in the reasoning path. The selection of these tokens is treated as a task-specific hyperparameter. For our experiments, they were chosen empirically based on a qualitative analysis of common thought-switching patterns observed in the model's outputs. Specifically, we select alternative and messy as thought-switching tokens in our implementation. This approach provides a simple yet effective mechanism that can be adapted to different tasks or model behaviors without extensive tuning. Besides, since LLM tokenizers can split a single word into

multiple subword tokens, the switching penalty is applied exclusively to the first subword token of a designated thought-switching word.

B.2 Grid Search of α and β

To ensure robust conclusions, we report Pass@1 results computed from 32 samples per instance. We calculate the weighted underthinking score for each instance over its 32 samples:

where s i is the i -th sample of the instance, and ξ UT ( s i ) = 0 when s i is correct.

By adjusting α and β , we can control the model's behavior to achieve the desired level of thought exploration. We performed a grid search with α values in [3 , 5 , 10 , 20 , 30] and β values in [300 , 400 , 500 , 600 , 700] using a development set that included the AIME 2022 and 2023 test sets. Table 5 lists the impact of varying the penalty strength α and penalty duration β on the model's accuracy. We observe that increasing the penalty strength α generally leads to an improvement in accuracy up to a certain threshold, after which the benefits plateau or even diminish. Adjusting the penalty duration β also significantly affects performance: At a lower penalty strength ( α = 3 ), increasing β from 300 to 600 results in accuracy gains from 35.2% to 39.8%, the highest observed accuracy in our experiment. Conversely, at higher penalty strengths ( α = 20 ), extending β beyond 300 leads to a decrease in accuracy, indicating that too long a penalty duration can hinder performance when combined with a strong penalty. We selected α = 3 and β = 600 for our subsequent experiments.

C Generalization to Multimodal Reasoning

To investigate if underthinking extends beyond text-only domains, we evaluated two vision-language models, GLM-4.1V-Thinking (Hong et al., 2025) and MiMo-VL7B-RL (Xiaomi, 2025), on the OE_MM_maths_en_COMP subset of

OlympiadBench (He et al., 2024). This benchmark contains competition-level mathematics problems that require synthesizing information from both text and images. As shown in Table 6, both models exhibit underthinking. This finding leads to two implications: first, our framework can be adapted to analyse multimodal reasoning, and second, it suggests that underthinking is a general reasoning failure not confined to a single modality.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: In this paper, we define the underthinking issue in long reasoning models, introduce a novel metric and propose a decoding approach with thought switching penalty. Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies underthinking, a phenomenon where a LRM frequently switches between reasoning thoughts before fully exploring promising paths. The authors found that incorrect responses often have a significantly higher number of thoughts, even though many of those thoughts could have led to correct answers. They further propose a UT score to quantitatively measure this issue. Based on these findings, the paper proposes a decoding strategy TIP that uses a thought-switching penalty. Experiments show that this simple strategy leads to improved performance for both standard decoding and best-of-N sampling while maintaining high token efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- This paper is well-motivated. It tackles an important problem in LRMs and provides insights into model performance and token efficiency.\n- The experiments and analysis are clean and easy to follow.\n- The proposed strategy, TIP, effectively mitigates the identified issues without significant overhead.\n\nWeakness\n- My main concern with this paper is the proposed UT score. If I understand correctly, the score measures the percentage of tokens required to achieve the first correct thought, but not how frequently the model switches between thoughts. It is still possible for a model to quickly reach the first correct thought (therefore having a low UT score), but then switch between different thoughts afterward. Therefore, I’m not sure if the UT score correctly captures what the authors intended to measure.\n- Additionally, based on Table 1, there is not a clear correlation between accuracy and the UT score, so it is unclear if the proposed metric can provide useful insights on the relation between underthinking issues and model performance. Instead, I think the \"switch tokens\" number and the interval measure in Table 2 would be more informative.\n\nQUESTIONS:\n- The TIP decoding strategy requires a predefined set of thought-switching tokens. How are these tokens selected? What if a word is tokenized into multiple tokens?\n- The legend for Figure 5 is confusing. There are dots in the figure that look different from the rest. What is the difference between these dots and the others?\n\nLIMITATIONS:\nThe authors briefly discuss the limitations in Section 2.3 (as stated in the checklist). I would suggest that the authors explicitly discuss this in a \"Limitations\" section to make things clear.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper investigates a phenomenon called “underthinking” in long reasoning models (LRMs)—a tendency to prematurely switch between different lines of reasoning rather than fully developing promising thoughts that could lead to correct answers. The authors observe that frequent thought-switching correlates with incorrect responses across three challenging mathematics benchmarks (MATH500-Hard, GPQA-Diamond, AIME 2024) and two open-source LRMs (QwQ-32B-Preview and DeepSeek-R1). Notably, many incorrect solutions still contain at least one early correct insight, which the models fail to pursue, instead expending large numbers of tokens on unproductive reasoning paths. To quantify this behavior, the authors introduce a novel metric called the underthinking score. To mitigate the issue, they propose a decoding strategy called TIP (Thought-switching Penalty), which requires no fine-tuning and consistently improves performance across the benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\n1) Strengths\n\n1a) Frequent Thought Switching in LRMs: The paper begins with an insightful observation: long reasoning models (LRMs) often switch between multiple solution strategies without fully committing to promising ones, even when such paths could lead to correct answers. This erratic behavior results in significant computational waste—incorrect answers tend to consume far more tokens than correct ones due to excessive idea-switching. This sheds new light on inefficiencies in tasks that demand long-term reasoning.\n\n1b) Underthinking Metric: To quantify this behavior, the authors introduce the underthinking score—a simple yet effective ratio that captures how many tokens are used after the first correct thought in failed solutions. This metric complements traditional accuracy by offering a way to measure reasoning inefficiency across models and datasets, enabling more fine-grained evaluations and tracking of future improvements.\n\n1c) TIP Decoding for Efficiency: Motivated by these findings, the authors propose TIP (Thought-switching Penalty) decoding, a method that discourages frequent shifts in reasoning. TIP requires no retraining and not only cuts token usage by more than half but also improves accuracy. Given the high computational costs of LRM-based reasoning, this offers a practical and impactful advance for improving efficiency and reducing latency.\n\n1d) Effective Qualitative Insights: In addition to quantitative results, the paper includes compelling qualitative examples that clearly illustrate the underthinking phenomenon. These case studies reinforce the paper’s central claims and enhance its overall effectiveness and clarity.\n\n2) Weaknesses\n\n2a) Limited Model-Size Coverage: The paper’s findings are based on two very large LRMs—QwQ-32B-Preview and DeepSeek-R1-671B (plus a preview variant)—tested on three math-focused benchmarks. However, the study does not explore whether the observed “underthinking” behavior persists in smaller models from the same families or in other widely used model series (e.g., LLaMA 2 models ranging from 7B to 70B). Including a more model sizes would help assess whether underthinking is a general phenomenon or one that primarily emerges at large scales.\n\n2b) Restricted Evaluation Scope: All experiments are limited to math reasoning and GPQA, with no investigation into whether the findings, the proposed underthinking metric, or the TIP decoding method generalize to other domains such as commonsense reasoning, dialogue, code generation, or multimodal tasks. Expanding the evaluation to diverse reasoning settings would significantly strengthen the broader applicability of the approach.\n\n2c) Evaluation Pipeline Limitations: The underthinking score relies on an auxiliary LLM-based classifier to determine whether intermediate thoughts are “correct.” However, this classifier achieves only ~82% accuracy on a small validation set, introducing uncertainty into the core metric. Increasing the size and robustness of this evaluation could enhance the credibility of the results and reduce measurement noise.\n\nQUESTIONS:\nPlease provide some details about generalization to new domains.\n\nLIMITATIONS:\nYes the authors have discussed some limitations.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work investigates the phenomenon of underthinking in current LRMs, where models frequently switch to different reasoning thoughts without fully exploring the path. It designs token efficiency metrics to measure such behaviors in QwQ-32B-Preview and Deepseek-R1-671B. To mitigate underthinking issues, this work develops Thought Switching Penalty to decrease the logit values during inference. The approach is evaluated on AIME-24 demonstrating that TIP reduces underthinking and improves accuracy .\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. This paper is well-written and content is clear and straightforward.\n2. The underthinking is an interesting concept to study the reasoning behaviors, providing a useful perspective in understanding LRMs.\n3. The work presents a thorough analysis of the correctness of intermediate thoughts and provides convincing empirical evidence to validate the presence of underthinking.\n\nWeaknesses:\n1. Table 2 is missing results for R1-Distill-Qwen-32B and Deepseek-R1 on MATH500-hard and GPQA Diamond. Also, from the table,  the results suggest that TIP yields more substantial accuracy improvements for models with lower baseline performance (such as QwQ on GPQA and AIME-24). Do you have any insight on this?\n\n2. (minor) The paper does not clearly explain why underthinking behavior appears in LRMs. A further discussion of its possible causes would help improve understanding.\n\nQUESTIONS:\n1. The TIP method works by penalizing the logits of certain keywords (like “alternatively”) that indicate thought switching. But some of these keywords might also reflect other useful cognitive behaviors, as discussed in a related work [1]. Do you think TIP might unintentionally suppress those behaviors too? Any insight or analysis would be helpful.\n\n[1] Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs.\n\nLIMITATIONS:\nThe authors briefly mention limitations in Section 2.3, but a separate section outlining them more clearly would be helpful.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
The paper identifies underthinking in long-reasoning LLMs (LRMs)—a tendency to hop between reasoning threads without sufficiently exploring promising ones. It (i) formalises an underthinking score that captures token-efficiency in incorrect answers 
, (ii) shows the prevalence of the phenomenon across three maths-reasoning benchmarks, and (iii) proposes Thought-switching-penalty (TIP), a decoding modification that down-weights tokens signalling a strategy switch. TIP improves Pass@1 by up to +5.8 pp without fine-tuning and synergises with self-consistency and laconic decoding

STRENGTHS AND WEAKNESSES:
Strength

1. To the best of my knowledge, this is the first paper that systematically characterise the "underthinking" in reasoning LLMs.

2. Therefore this is also the first paper that proposed the SUT score, a valid metric for quantifying the underthinking behaviours.

3. This paper really expanded my understanding of the LLMs' limitations beyond the simple accuracy metrics.

Weakness

1. The correct thought score is judged by the distilled LLMs, which may introduce bias.

2. Rather than saying as a weakness point, I am a little bit curious that if this kind of analyse toolkit could be easily adapt to coding and multi-modal reasoning problems?

QUESTIONS:
See the weakness part.

LIMITATIONS:
yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03