Title: Reasoning on a Spectrum: Aligning LLMs to System 1 and System 2 Thinking
Venue: NeurIPS 2025
Accepted: no
Keywords: Alignment

System 1 and System 2 thinking

Cognitive heuristics

LLM

NLP
Abstract: Large language models (LLMs) demonstrate remarkable reasoning capabilities, yet their reliance on step-by-step reasoning can make them brittle when tasks do not align with such structured approaches. In contrast, human cognition flexibly alternates between fast, intuitive reasoning (System 1) and slow, analytical reasoning (System 2), depending on context. To bridge this gap, we curate a dataset of 2K examples, each with valid responses from both reasoning styles, and explicitly align LLMs with System 1 and System 2 reasoning. Evaluations across diverse reasoning benchmarks reveal an accuracy-efficiency trade-off: System 2-aligned models excel in arithmetic and symbolic reasoning, while System 1-aligned models perform better in commonsense tasks. A mechanistic analysis of model responses shows that System 1 models employ more definitive answers, whereas System 2 models demonstrate greater uncertainty. Interpolating between these extremes produces a monotonic transition in reasoning accuracy, preserving coherence. This work challenges the assumption that step-by-step reasoning is always optimal and highlights the need for adapting reasoning strategies based on task demands.

Abstract

Large language models (LLMs) demonstrate remarkable reasoning capabilities, yet their reliance on step-by-step reasoning can make them brittle when tasks do not align with such structured approaches. In contrast, human cognition flexibly alternates between fast, intuitive reasoning (System 1) and slow, analytical reasoning (System 2), depending on context. To bridge this gap, we curate a dataset of 2K examples, each with valid responses from both reasoning styles, and explicitly align LLMs with System 1 and System 2 reasoning. Evaluations across diverse reasoning benchmarks reveal an accuracy-efficiency trade-off: System 2-aligned models excel in arithmetic and symbolic reasoning, while System 1-aligned models perform better in commonsense tasks. A mechanistic analysis of model responses shows that System 1 models employ more definitive answers, whereas System 2 models demonstrate greater uncertainty. Interpolating between these extremes produces a monotonic transition in reasoning accuracy, preserving coherence. This work challenges the assumption that step-by-step reasoning is always optimal and highlights the need for adapting reasoning strategies based on task demands. 1

1 Introduction

LLMs have demonstrated remarkable reasoning capabilities, often achieving near-human or even superhuman performance (Huang and Chang, 2023). These advances have largely been driven by techniques that simulate step-by-step, deliberative reasoning, such as Chain-of-Thought (CoT) prompting and inference-time interventions (Wei et al., 2022b; Wang et al., 2022). Given their success, such methods are increasingly integrated into LLM training (Chung et al., 2024), reinforcing explicit, structured reasoning regardless of the task necessity. However, the increasing focus on step-by-step reasoning has revealed limitations such as brittle generalization, particularly in tasks requiring nuanced judgment (Delétang et al., 2023), logical consistency (Jiang et al., 2024), or adaptability to uncertainty (Mirzadeh et al., 2024). Similarly, recent analyses frame this issue as 'overthinking': Cuadron et al. (2025); Chen et al. (2024) demonstrate that excessive deliberation can hamper decision-making. This problem appears in LLMs' responses to simple factual queries, where they often generate unnecessarily explanations instead of direct responses (Wang et al., 2023).

This focus on explicit, structured reasoning highlights a key difference between LLMs and human cognition: while LLMs are being pushed towards a single mode of processing, human reasoning is far more nuanced. Rather than a monolithic process, human reasoning emerges from a sophisticated suite of cognitive tools evolved to tackle a spectrum of computational problems. This spectrum of human reasoning encompasses both automatic and reflective processes, a key insight recognized across

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diverse fields from behavioral economics to psychology and neuroscience (Daw et al., 2005; Dolan and Dayan, 2013; Balleine and Dickinson, 1998). On one end lie computationally light problems demanding rapid, intuitive judgments (e.g., instinctively dodging a speeding car), handled by the reflexive 'System 1.' On the other end are heavy problems requiring deliberate, step-by-step analysis, managed by the reflective 'System 2' (Kahneman, 2011; Stanovich and West, 2000). This dualprocess system allows us to dynamically shift between modes depending on the task, balancing speed and accuracy (Evans and Stanovich, 2013). Extensive work in neuroscience in the past two decades links the dual-process framework and human decision strategies, which depicts decision-making on a spectrum between a fast but reflexive habitual decision strategy and a reflective goal-directed strategy (Daw et al., 2005; Dolan and Dayan, 2013). Experimental work in neuroscience is built on the relative advantages of these two strategies, the separate but overlapping neural structures supporting them, and the circumstances under which each system is deployed in the brain (Daw et al., 2011; Schad et al., 2020; Piray and Daw, 2021). Given the evolutionary advantage of humans in switching between fast and slow thinking to balance speed, efficiency, and accuracy, exploring LLMs through the lens of System 1 and System 2 reasoning offers a powerful way to address their current limitations.

While recent studies explore whether LLMs exhibit System 1 and System 2 behaviors (Hagendorff et al., 2023; Pan et al., 2024) or propose hybrid models (Yang et al., 2024; Deng et al., 2024), most prior work implicitly assumes that structured, deliberative reasoning is universally superior. Even research suggesting LLMs' capacity for both reasoning modes (Wang and Zhou, 2024) largely overlooks the crucial question of when each mode is indeed advantageous. The assumption that a single 'best' reasoning strategy can apply across all contexts is a fundamental simplification that limits current approaches in LLM development. This assumption prevents LLMs from achieving true cognitive flexibility, hindering their ability to adapt their reasoning processes to diverse situations.

To address this gap, we explicitly align LLMs with System 1 and System 2 reasoning and evaluate their reasoning capabilities and behaviors across a range of reasoning benchmarks. Our approach involves designing an experimental setup where both thinking styles can produce valid responses but follow distinct paths, one leveraging intuitive heuristics, and the other prioritizing deliberate, step-by-step reasoning. By systematically assessing how reasoning styles and cognitive biases affect downstream task performance, we provide insights into when intuitive heuristics or structured deliberation are most effective, and highlight the trade-offs between accuracy and efficiency in LLMs.

Specifically, as demonstrated in Figure 1, we first curate a dataset of 2,000 reasoning questions, where each problem has both a fast, heuristic-driven (System 1) response and a deliberative, structured (System 2) response, grounded in 10 different cognitive heuristics (Tversky and Kahneman, 1974). We then explicitly align LLMs with either System 1 or System 2 type responses and evaluate these models on diverse reasoning benchmarks. Our findings reveal a structured accuracy-efficiency tradeoff and demonstrate that different reasoning paradigms in LLMs excel at different types of tasks, mirroring how humans selectively rely on fast or slow thinking depending on task demands: System 2-aligned models consistently outperform instruction-tuned and CoT prompt baselines in arithmetic and symbolic reasoning, demonstrating superior multi-step inference, but generating more extended token-intensive responses. Conversely, System 1-aligned models generate more succinct responses and excel at commonsense reasoning, where heuristic shortcuts are effective. Importantly, unlike CoT models, which always engage in structured reasoning regardless of necessity, our models provide an explicit way to study when different reasoning styles are beneficial, mirroring the well-known efficiency-accuracy trade-off in human cognition (Keramati et al., 2011; Mattar and Daw, 2018). By framing LLM reasoning as a structured and adaptable process, rather than simply an ability to achieve higher benchmark scores, this work highlights the importance of selecting the right reasoning strategy for a given task. This perspective not only aligns LLM reasoning more closely with human cognition but also paves the way for more flexible, efficient, and robust reasoning systems, setting a foundation for future advancements in LLM reasoning.

2 Related Work

2.1 Reasoning in LLMs

Driven by extensive research highlighting the strengths and weaknesses of LLM reasoning abili86 ties (e.g., Huang and Chang, 2022; Mondorf and Plank, 2024; Valmeekam et al., 2022; Parmar 87

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et al., 2024; Sourati et al., 2024), recent efforts to enhance these capabilities have largely focused on prompting techniques (Brown et al., 2020), ranging from zero-shot prompting with explicit instructions (Kojima et al., 2022; Wang et al., 2023; Zhou et al., 2024b) to few-shot prompting with step-by-step examples (Wei et al., 2022b). Wang and Zhou (2024) take CoT prompting even one step further and demonstrate that CoT reasoning paths can be elicited from pre-trained LLMs by simply altering the decoding process without the use of a specific prompt. Related approaches, such as self-consistency decoding Wang et al. (2022), explore how diverse reasoning paths can enhance robustness, aligning with deliberative aspects of System 2 reasoning. Tree of Thought (ToT; Yao et al., 2024) generalizes over CoT and allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make a global choice. Another alternative way of increasing the reasoning abilities of LLMs is through instruction tuning on a substantial amount of CoT reasoning data Chung et al. (2024); Huang et al. (2022) or distillation Magister et al. (2022). By training LLMs on a large-scale CoT dataset, models can internalize step-by-step reasoning, potentially enhancing their performance across diverse benchmarks without relying solely on prompting techniques. Concurrent studies have identified an 'overthinking' phenomenon in LLMs, where models produce excessively detailed or unnecessarily elaborate reasoning steps (Chen et al., 2024; Cuadron et al., 2025).

2.2 Dual-Process Theory in NLP

Dual-process theories, widely studied in psychology, distinguish between fast, intuitive reasoning (System 1) and slow, deliberate reasoning (System 2). While these theories have long explained the spectrum of human reasoning, their application in NLP remains underexplored. Existing research falls into two main categories: (1) analyzing LLMs' reasoning through dual-process theory, identifying similarities and differences between LLMs and human reasoning, and (2) developing models with dual-process mechanisms to enhance LLM reasoning and leverage the benefits of both systems.

Analyzing LLMs' reasoning through dual-process theory. Researchers have investigated whether LLMs exhibit reasoning behaviors aligned with System 1 and System 2, particularly in terms of cognitive human-like errors and biases (Hagendorff et al., 2023; Booch et al., 2021; Pan et al., 2024; Echterhoff et al., 2024; Zeng et al., 2024). Hagendorff et al. (2023) examine cognitive heuristics in LLMs, showing that newer models exhibit fewer errors characteristic of System 1 thinking. Booch et al. (2021) discuss fundamental questions regarding the role of dual-process theory in machine learning but leave practical implementation as an open problem. Most of these studies evaluate LLMs on benchmarks where System 2 reasoning is assumed to be superior, portraying intuitive responses as erroneous, even though such rapid, heuristic-driven judgments are often crucial for efficient and effective reasoning in real-world scenarios. In contrast, by analyzing models aligned with System 1 and System 2 reasoning using a carefully curated dataset where both response types are valid, we offer a more nuanced understanding of how this alignment influences broader model behavior.

Incorporating dual-process theory in NLP models. Several studies have integrated dual-process125 inspired reasoning into LLMs. Some works combine intuitive (fast) and deliberate (slow) components 126 to improve reasoning (He et al., 2024; Liu et al., 2022; Hua and Zhang, 2022; Pan et al., 2024), while 127 others optimize reasoning efficiency by distilling System 2 insights into System 1 models (Yang et al., 128 2024; Deng et al., 2024; Yu et al., 2024). Additionally, research has leveraged System 2 reasoning to 129 mitigate biases associated with System 1 heuristics, improving fairness and robustness (Furniturewala 130 et al., 2024; Kamruzzaman and Kim, 2024; Weston and Sukhbaatar, 2023). While prior work largely 131 frames System 2 reasoning as superior or explicitly builds dual-process components within models, 132 our approach investigates the implicit effects of aligning LLMs to System 1 or System 2 responses. 133 By analyzing how these heuristics influence general reasoning capabilities, we address a gap in 134 the literature and provide new insights into the broader cognitive behaviors of LLMs that have 135 implications for how unseen properties of data that LLMs are trained on can affect their capabilities. 136

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(A) Dataset

(B) Approach

Question

CommonSense

If you estimate a number quickly, do you adjust it later when you

getmoreaccuratedata?

System1

Answer

System2

Answer

Arithmetic

X

X

System1Answer

System2Answer

Igenerallystickwithmyinitial

Irevisemyestimatesbasedon

CommonSense

estimateandrarelydeviate

updatedandaccuratedata

System1

System2

Arithmetic

from it...

sinceitcanreduceserrors...

Answer

Answer

3 Method

3.1 Aligning LLMs to System 1 & System 2 Thinking

We formalize the modeling of fast and slow thinking as an alignment problem using a curated dataset in which each reasoning question is paired with both a System 1 (intuitive) and a System 2 (analytical) response (see Section 3.2). We align LLMs to either reasoning style via a preference-based training approach: for System 1 alignment, the intuitive response is designated as the preferred (winner) and the analytical response as the non-preferred (loser); for System 2 alignment, this preference is reversed, treating the analytical response as the winner and the intuitive response as the loser.

This approach is effective for two key reasons. First, our aim is not to introduce new knowledge or instructions but rather to shape the model's reasoning process based on existing capabilities. Second, previous research has shown that prompt engineering can guide LLMs toward System 2 reasoning (Wei et al., 2022a) or System 1 reasoning (Zhou et al., 2024a), suggesting that LLMs already have both reasoning abilities. Therefore, instead of creating new reasoning pathways, we guide the model to distinguish between intuitive and analytical reasoning processes without altering its underlying knowledge. The next section describes the dataset creation process that enables this training setup.

3.2 Dataset of System 1 & System 2 Thinking

Our curated dataset consists of 2,000 questions designed to elicit two distinct reasoning styles in English: one intuitive and rapid, reflecting cognitive shortcuts (System 1), and the other deliberate and analytical (System 2). This dual structure allows us to study the distinct mechanisms underlying System 1 and System 2 reasoning (Kahneman, 2011; Stanovich and West, 2000; Evans and Stanovich, 2013). The dataset was created in three key phases: Generation, Refinement, and Validation.

Generation. Cognitive heuristics provide a practical foundation for distinguishing between System 1 and System 2 reasoning, where both yield valid but behaviorally distinct responses (Kahneman, 2011). To construct our dataset, we adopted a human-in-the-loop pipeline that leverages GPT-4o (Hurst et al., 2024) to scale up the number of high-quality reasoning examples. In line with recent work on dataset creation using LLMs and few-shot prompting (Xu et al., 2023; Wang et al., 2022), we used a one-shot prompting setup, where each generation is guided by a carefully selected example grounded in a particular cognitive heuristic. These seed examples were authored by domain experts (see Appendix D) and span 10 well-known heuristics from Kahneman (2011) (Appendix C). For each, experts provided a reasoning question accompanied by both a System 1 (heuristic) and System 2 (deliberative) response. During expansion, the prompt included the formal definition of each heuristic, a description of how both systems typically approach it, and the expert-written example. This setup enabled the model to generate new reasoning items aligned with distinct cognitive patterns. Full prompt details are provided in Appendix F, and expert-authored examples are shown in Appendix E.

Refinement. As a byproduct of the data generation process, System 2 outputs were significantly longer and more detailed-reflecting their step-by-step reasoning style, while System 1 outputs were shorter and more direct; this length difference was confirmed using Welch's t -test, t (2090 . 1) = -184 . 74 , p < . 001 , d = -5 . 84 . Prior work demonstrates that alignment methods can rely on superficial cues, such as output length, favoring longer responses even when they offer no real reasoning advantage (Singhal et al., 2023). To prevent this bias, we use zero-shot prompting with

GPT-4o to match the lengths of our System 1 and System 2 outputs while preserving their content. 177 Adjustments were applied only when there was a significant length disparity. More details about 178 the prompt and the length disparity threshold are described in Appendix J. By reducing the length 179 disparity, we minimized any preference for System 2 outputs arising from their longer responses. 180 After adjustment, System 1 outputs had an average length of 82.19 tokens, while System 2 outputs 181 averaged 83.93 tokens. A two one-sided t-test (TOST) confirmed the equivalence of post-adjustment 182 lengths across various token counts as equivalence margins (see Appendix I), indicating that the 183 adjustment effectively eliminated significant length differences between the two response types. 184

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Verification. Prior works show that high-quality, expert-supervised datasets of this scale are common and effective for fine-tuning LLMs (Xiao et al., 2024; Dumpala et al., 2024; Li et al., 2024). Following this precedent to ensure data quality, we had our domain experts conform all generated data to formal definitions of System 1 and System 2 thinking, and ensured that the dataset covers the intended set of cognitive heuristics across varied subject areas. In this process, the experts manually revised approximately 20% of the responses. We further verified the breadth of topic coverage via topic modeling; see Appendix G for details. A subset of the curated dataset is shown in Table 1.

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4 Experiments Setup

4.1 Alignment Algorithm

To implement the alignment strategy for System 1 and System 2 reasoning, we utilize two offline preference optimization methods, namely, Direct Preference Optimization (DPO; Rafailov et al., 2024) and Simple Preference Optimization (SimPO; Meng et al., 2024), because (i) their offline formulation removes the costly on-policy sampling loop, yielding a simpler and more computeefficient training pipeline, and (ii) our hand-crafted preference pairs capture fine-grained relational signals that would likely be blurred by online-generated pairs.

DPO is an offline alignment method that fine-tunes LLMs by comparing the preferred and disfavored outputs of a model against a reference model, optimizing preferences without requiring a separate reward model. As a prominent method in preference optimization, DPO has gained traction for its stability and efficiency, making it a widely adopted alternative to Reinforcement Learning from Human Feedback (RLHF; Ouyang et al., 2022). SimPO builds on the principles of DPO but introduces a reference-free approach to preference optimization. Instead of requiring a separate reference model, SimPO aligns responses by directly optimizing preference signals within the model itself. This makes it computationally more efficient and removes the dependency on an external reference model, offering a streamlined alternative for aligning LLMs to a specific preference.

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4.2 Benchmarks

We evaluate our System 1 and System 2 models using 13 reasoning benchmarks across three different categories: (1) arithmetic reasoning: MultiArith (Roy and Roth, 2015), GSM8K (Cobbe et al., 2021), AddSub Hosseini et al. (2014), AQUA-RAT (Ling et al., 2017), SingleEq (Koncel-Kedziorski et al., 2015), and SVAMP (Patel et al., 2021); (2) commonsense reasoning: CSQA (Talmor et al., 2019), StrategyQA (Geva et al., 2021), PIQA (Bisk et al., 2020), SIQA (Sap et al., 2019), and COM2SENSE (Singh et al., 2021); (3) symbolic reasoning: Last Letter Concatenation and Coin Flip Wei et al. (2022b). More details about the benchmarks are in Appendix H.

Following Kong et al. (2024), our evaluation follows a two-stage process. In the first stage, we present benchmark questions to model and record its responses. In the second stage, we prompt the model with the original question, its initial response, and benchmark-specific instructions to ensure the output is formatted as required. See Appendix K for each benchmark's instructions.

4.3 Implementation Details

We use Llama-3-8B-Instruct (AI@Meta, 2024) and Mistral-7B-Instruct-v0.1 (Jiang et al., 2023) as SFT models for alignment. Following Kojima et al. (2023), we compare the performance of these aligned models against their instruction-tuned counterparts under zero-shot and zero-shot CoT prompting (additional details in Appendix L). To analyze the model's behavior along the System 1 to System 2 reasoning spectrum, we train seven intermediate models, where the winner responses are mixed at predefined ratios between System 1 and System 2. This structured interpolation allows us to systematically assess whether the transition between reasoning styles is discrete or gradual.

5 Results

5.1 Distinct Strengths of System 1 & System 2 Models

Table 2 shows a comparison of exact matching accuracy across 13 benchmarks for Llama and Mistral. Specifically, we compare the base models with the System 1 and System 2 variants, and include results for CoT prompting for reference. Our findings reveal distinct performance trends for the System 1 and System 2 models, highlighting their respective strengths in different reasoning benchmarks.

In all arithmetic benchmarks (MultiArith, GSM8K, AddSub, AQuA, and SingleEq), System 2 models outperformed both the base model and their System 1 counterpart, evident for both Llama and Mistral. This improvement is most significant in the AddSub and SingleEq benchmarks. Similarly, System 2 models outperformed System 1 models in nearly all symbolic reasoning benchmarks (Coin, Letter), which require pattern recognition and logical structuring, further validating the idea that deliberative, slow-thinking models enhance performance in structured reasoning. While both approaches achieve high accuracy, System 1's heuristic shortcuts introduce small but systematic errors that System 2's deliberate, stepwise computations tend to avoid, such as rounding the number or adding numbers without checking. This is further supported by our AddSub analysis (see Appendix O).

Conversely, System 1 models excelled both their System 2 counterparts and the base model as well as the CoT variant on all commonsense reasoning benchmarks (CSQA, StrategyQA, PIQA, SIQA, COM2SENSE), which depend on intuitive judgments and heuristic shortcuts. While System 2 reasoning is correct, its deliberate nature can often lead to overthinking, producing overly cautious or extensively interpretive responses that diverge from typical human reactions in rapid, intuitive situations. For example, when asked what a kindergarten teacher does before nap time, System 2 suggests 'encourage quiet behavior' instead of 'tell a story,' or predicts 'laughter' rather than 'fight' if you surprise an angry person. As shown in Appendix O, this preference for completeness over contextual fit makes System 2 less reliable for quick, socially grounded tasks.

When comparing Llama and Mistral, Llama models generally achieved higher accuracy across all benchmarks. This suggests that Llama may have stronger foundational reasoning capabilities, which are further enhanced by the System 2 and System 1 alignment. Moreover, instruction-tuned models equipped with the CoT prompt exhibited only marginal differences compared to their base counterparts because step-by-step reasoning has already been internalized during pretraining on CoTstyle data (AI@Meta, 2024), reducing the need for explicit prompting. Based on this observation, we use the base Llama model as our primary baseline in subsequent experiments.

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Arithmetic

MultiArith

AddSub

AQuA

SingleEq

SVAMP

Coin

Letter

CSQA

Strategy

PIQA

SIQA

COM2SENSE

System 2

DPO

98.67

(+1.0)

79.37

(+0.88)

89.87

(+7.4)

49.21

(+0.39)

94.37

(+3.65)

85.4

(+4.9)

93.8

(-0.4)

86.2

(+2.2)

71.42

(0)

60.87

(-6.68)

81.15

(-2.01)

67.93

(-3.19)

76.42

(-2.6)

SIMPO

97.83

(+0.16)

79.38

(+0.89)

90.13

(+7.66)

54.72

(+6.78)

94.49

(+3.77)

81.7

(+1.2)

94.4

(+0.2)

84.8

(+0.8)

69.62

(-1.8)

67.38

(-0.17)

81.49

(-1.67)

69.16

(-1.96)

78.21

(-0.81)

Llama-3

97.67

78.49

82.47

48.82

90.72

80.5

94.2

84

71.42

67.55

83.16

71.12

79.02

Llama-3-CoT

97.83

78.54

82.03

49.21

88.19

80.9

94.8

84.2

71.58

67.38

83.34

70.97

79.86

System 1

DPO

98.5

(+0.83)

77.01

(-1.48)

80.76

(-1.71)

46.46

(-2.36)

77.24

(-13.48)

78

(-2.5)

93.4

(-0.8)

83.8

(-0.2)

72.81

(+1.39)

68.21

(+0.66)

83.94

(+0.78)

72.16

(+1.04)

79.99

(+0.97)

SIMPO

97.5

(-0.17)

77.79

(-0.7)

80.51

(-1.96)

48.03

(-0.79)

87.4

(-3.32)

79.3

(-1.2)

90

(-4.2)

83.8

(-0.2)

72.32

(+0.9)

67.73

(+0.18)

83.35

(+0.19)

71.67

(+0.55)

81.46

(+2.44)

System 2

DPO

78.83

(+1.16)

56.45

(+1.47)

81.27

(+6.79)

32.68

(+1.19)

84.84

(+0.98)

69.1

(+3.4)

41

(-2.2)

8.6

(+8)

62.82

(-3.44)

56.81

(-8.6)

80.49

(0)

57.77

(-2.24)

66.73

(-1.64)

SIMPO

78.3

(+0.63)

55.42

(+0.53)

82.28

(+7.8)

34.25

(+2.76)

86.81

(+2.95)

68.5

(+2.8)

45.4

(+2.2)

7.8

(+6.2)

64.78

(-1.48)

63.75

(-1.66)

82.07

(-0.46)

59.82

(-0.19)

68.15

(-0.22)

Mistral

77.67

54.89

79.75

31.49

83.86

66.26

43.2

1.6

66.26

65.41

82.53

60.01

68.37

Mistral-CoT

78.3

54.96

80.25

33.07

83.66

67.8

43.8

1.6

66.18

65.49

82.21

60.76

69.01

System 1

DPO

77.5

(-0.17)

51.4

(-3.49)

79.49

(-0.26)

29.53

(-1.96)

83.07

(-0.79)

67.4

(-0.2)

40.4

(-2.8)

0

(-1.6)

67.4

(+1.14)

65.49

(+0.08)

83.22

(+0.69)

60.01

(0)

70.83

(+2.46)

SIMPO

77

(-0.67)

53.61

(-1.28)

78.73

(-1.02)

31.1

(-0.39)

83.67

(-0.19)

67.3

(-0.3)

43

(-0.2)

0

(-1.6)

67.32

(+1.06)

65.51

(+0.1)

82.84

(+1.31)

60.93

(+0.92)

69.13

(+0.76)

In summary, our results showcase that System 2 models excel in structured, multi-step reasoning such as arithmetic and symbolic reasoning, while System 1 models are effective in intuitive and commonsense reasoning benchmarks. These findings highlight the significant potential of dualprocess alignment for boosting LLM performance across a diverse range of reasoning paradigms.

5.2 Length Differences Across Reasoning Styles

A recent trend in LLM performance, exemplified by models such as DeepSeek R1 (Muennighoff et al., 2025), is that achieving stronger benchmark results often correlates with producing longer reasoning chains, even if not explicitly trained to do so. This correlation raises the question of whether such verbose responses truly reflect enhanced reasoning capabilities or if they are simply a formatting artifact of current high-performing models. In our studies, this concern is particularly relevant for System 2 models, which are expected to behave more deliberatively. To investigate this, we analyze output lengths across the two-stage prompting setup described in Section 4.2.

As shown in Figure 2, System 2-aligned models generate significantly longer responses than their System 1 counterparts, relative to the Llama baseline, under both alignment methods, DPO ( t (8836) = 57 . 14 , p < . 001 ) and SimPO ( t (8586) = 9 . 833 , p < . 001 ). This difference emerges specifically in the second stage, where models are prompted to finalize their responses, while response lengths remain comparable in the first stage, where both models are simply asked to reason. Although both models were trained on equal-length preference pairs (Section 3.2), System 2 models still tend to elaborate more during finalization, consistent with their alignment toward deliberative reasoning.

While longer reasoning chains are often associated with stronger performance, our findings suggest that this extended reasoning can also introduce inefficiencies or even degrade quality in contexts where concise, heuristic-driven reasoning is more appropriate. In particular, tasks requiring commonsense or intuitive judgments are often better handled by System 1 models, which respond more directly. This highlights a central insight of our study: extended reasoning is not universally beneficial, and reasoning strategies must be evaluated in relation to the task.

5.3 Moving from Fast to Slow Thinking

In the previous analysis, System 1 and System 2 models can be viewed as endpoints of a broader spectrum of reasoning strategies. Paralleling approaches in cognitive psychology (Daw et al., 2011;

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Piray and Daw, 2021), we explored this spectrum by constructing interpolated models-blending System 1 and System 2 preferred answers at varying ratios in the alignment dataset. Figure 3 demonstrates a consistent, monotonic transition in accuracy across representative benchmarks from three reasoning categories (all r 2 > 0 . 9 , p < 0 . 001 ), a pattern visible across all benchmarks (see Appendix M). While arithmetic and symbolic reasoning benchmarks exhibit a steady increase in accuracy moving toward System 2 thinking, commonsense reasoning benchmarks show the opposite trend, with accuracy increasing as models rely more on System 1 reasoning. This trade-off highlights that both reasoning styles offer unique advantages, with System 2 excelling in structured, multi-step problem-solving and System 1 providing efficient, adaptable responses in intuitive scenarios. These findings strengthen the importance of task-dependent reasoning strategies that leverage the strengths of both System 1 and System 2 thinking. Critically, there are no sudden drops or fluctuations in performance when transitioning between reasoning styles. This stability indicates that the shift from System 1 to System 2 reasoning is gradual and predictable, without any unexpected anomalies. This observation reinforces the idea that LLMs can be strategically guided toward different reasoning styles, allowing for more adaptive problem-solving.

5.4 Reasoning & Uncertainty

A key insight from psychology and neuroscience is that System 1 operates on confident heuristics, providing quick, intuitive judgments, while System 2 engages in more deliberate, analytical thought, accurately assessing the uncertainty associated with its conclusions (Daw et al., 2005; Lee et al., 2014; Keramati et al., 2011; Xu, 2021). To examine uncertainty and confidence, we consider three different characteristics: 1) token-level uncertainty; 2) the presence of hedge words in model output (Lakoff, 1973; Ott, 2018); and 3) definitive commitment to responses in System 1 versus System 2.

Plot A in Figure 4 shows that System 2 models consistently generate tokens with lower confidence than System 1 models, based on token-level uncertainty from logits. This trend holds across arithmetic t (4075) = 54 . 53 , p < . 001 , symbolic t (999) = 42 . 53 , p < . 001 , and commonsense t (3510) = 106 . 86 , p < . 001 benchmarks. Additionally, we analyzed surface-level uncertainty in model reasoning by examining word choices. Figure 4, Plot B shows System 2-aligned models use significantly more hedge words, in arithmetic t (4075) = 22 . 03 , p < . 001 and commonsense t (3510) = 21 . 49 , p < . 001 when models reiterate their reasoning. While increased uncertainty enhances analytical reasoning, it may hinder tasks requiring rapid, intuitive judgments. To assess early-stage response conclusiveness, we used LLM-as-Judge (Zheng et al., 2023) as detailed in Appendix N. Figure 4, Plot C shows System 1 models provide significantly more definitive responses than System 2 models in commonsense reasoning, McNemar's χ 2 (1 , 400) = 20 . 0 , p < . 001 , regardless of where in the response the definitive responses is reached (see Appendix N).

This analysis reinforces the idea that different reasoning styles are suited to different tasks. Greater uncertainty in models' generated reasoning suggests that System 2 models can explore alternative reasoning paths more effectively. This uncertainty is reflected in both their model output probabilities and word choices. System 2 models' superior performance in arithmetic benchmarks highlights the benefits of deliberate, effortful processing in tasks that demand exploration and uncertainty. On the other hand, the greater tendency of System 1 models to commit to responses in a more definitive way aligns with their advantage in tasks requiring rapid and intuitive judgments. This behavior is observed exclusively in commonsense reasoning, where quick, decisive responses are advantageous-a trend supported by human studies (Byrd, 2022) and confirmed by our findings in Section 5.1. However, it does not appear in other benchmarks (see Appendix N), suggesting that the activation of a particular reasoning style is context-dependent and influenced by task demands.

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6 Conclusion

A central question in current LLM development is whether structured, step-by-step reasoning is 342 always beneficial, or whether a more flexible range of reasoning strategies is needed. Inspired by 343 dual-process theories of human cognition, we studied LLMs explicitly aligned with System 1 and 344 System 2 thinking, representing fast, heuristic reasoning and slow, analytical reasoning, respectively. 345 Our findings indicate that, much like in human cognition, reasoning in LLMs is not a one-size-fits346 all solution: different reasoning modes are effective in different contexts and downstream tasks. 347 System 2 excels in arithmetic and symbolic reasoning, while System 1 is more effective and accurate 348 in commonsense reasoning (Section 5.1). Training intermediate models with blended ratios of 349 preferred System 1 and System 2 responses revealed smooth, monotonic shifts in performance 350 across benchmarks (Section 5.3), supporting the view that LLM reasoning lies on a continuous, 351 tunable spectrum rather than a binary divide. Additionally, System 1 models generate responses with 352 fewer tokens, highlighting its efficiency in decision-making (Section 5.2). Finally, our analysis in 353 Section 5.4 illustrated that System 2 models exhibit greater uncertainty throughout the reasoning 354 process, potentially enabling them to engage in more structured, step-by-step problem-solving. In 355 contrast, System 1 models display higher confidence, allowing them to reach responses faster, which 356 is particularly advantageous for tasks requiring rapid, intuitive judgments. 357

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Beyond these empirical findings, our study aligns with broader principles observed across cognitive science and neuroscience. The observation that System 1 models generate faster responses echoes established theories in human cognition, where intuitive, heuristic-driven thinking allows for rapid decision-making. Similarly, the higher uncertainty exhibited by System 2 models aligns with neuroscience findings that deliberate reasoning involves increased cognitive load and self-monitoring mechanisms. These parallels suggest that LLMs, when properly aligned, can mirror key aspects of human cognition, offering new insights into both artificial and natural intelligence.

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Our work bridges between LLM development and cognitive science, highlighting how we can enable efficiency-accuracy trade-offs in LLMs, similar to those long observed in human cognition. We align models with reasoning behaviors that follow well-known cognitive heuristics, which humans use in everyday thinking, like System 1's rapid, intuitive judgments and System 2's deliberate, analytical thought, and show they can follow the dynamic interplay between fast and slow thinking. This alignment not only informs more sophisticated training and evaluation strategies but also suggests that future LLMs can be designed to possess a more cognitively grounded flexibility, allowing them to adapt their reasoning as effectively as humans do when faced with diverse task demands. Finally, models that reason in ways that are cognitively interpretable, mirroring the human brain's strategies for learning, decision making, and inference, may also be more predictable, steerable, and trustworthy in deployment. In this light, dual-process alignment connects cognitive science and neuroscience with model capabilities, enabling future LLMs to reason more like humans, not just in what they conclude, but in how they get there.

This paper is a first step toward adaptive reasoning in LLMs, where models can dynamically shift 378 between heuristic and deliberative thinking based on task demands. Furthermore, understanding how 379 to optimally balance speed and accuracy in LLMs can have significant implications for real-world 380 applications, from conversational agents to automated decision-making systems. In practice, this 381 approach could let us deliberately trade off answer quality for faster responses by choosing fewer 382 reasoning steps when time is critical. 383

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References

Abdin, M., Aneja, J., Behl, H., Bubeck, S., Eldan, R., Gunasekar, S., Harrison, M., Hewett, R. J., Javaheripi, M., Kauffmann, P., et al. (2024). Phi-4 technical report. arXiv preprint arXiv:2412.08905 .

AI@Meta (2024). Llama 3 model card.

Alizadeh, M., Kubli, M., Samei, Z., Dehghani, S., Bermeo, J. D., Korobeynikova, M., and Gilardi, F. (2023). Open-source large language models outperform crowd workers and approach chatgpt in text-annotation tasks. arXiv preprint arXiv:2307.02179 , 42.

Balleine, B. W. and Dickinson, A. (1998). Goal-directed instrumental action: contingency and incentive learning and their cortical substrates. Neuropharmacology , 37(4-5):407-419.

Bisk, Y., Zellers, R., Bras, R. L., Gao, J., and Choi, Y. (2020). Piqa: Reasoning about physical commonsense in natural language. In Thirty-Fourth AAAI Conference on Artificial Intelligence .

Booch, G., Fabiano, F., Horesh, L., Kate, K., Lenchner, J., Linck, N., Loreggia, A., Murgesan, K., Mattei, N., Rossi, F., et al. (2021). Thinking fast and slow in ai. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 35, pages 15042-15046.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in neural information processing systems , 33:1877-1901.

Byrd, N. (2022). Bounded reflectivism and epistemic identity. Metaphilosophy , 53(1):53-69.

Chen, X., Xu, J., Liang, T., He, Z., Pang, J., Yu, D., Song, L., Liu, Q., Zhou, M., Zhang, Z., et al. (2024). Do not think that much for 2+ 3=? on the overthinking of o1-like llms. arXiv preprint arXiv:2412.21187 .

Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2024). Scaling instruction-finetuned language models. Journal of Machine Learning Research , 25(70):1-53.

Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J. (2021). Training verifiers to solve math word problems.

Cuadron, A., Li, D., Ma, W., Wang, X., Wang, Y., Zhuang, S., Liu, S., Schroeder, L. G., Xia, T., Mao, H., et al. (2025). The danger of overthinking: Examining the reasoning-action dilemma in agentic tasks. arXiv preprint arXiv:2502.08235 .

Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P., and Dolan, R. J. (2011). Model-based influences on humans' choices and striatal prediction errors. Neuron , 69(6):1204-1215.

Daw, N. D., Niv, Y., and Dayan, P. (2005). Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature neuroscience , 8(12):1704-1711.

Delétang, G., Ruoss, A., Grau-Moya, J., Genewein, T., Wenliang, L. K., Catt, E., Cundy, C., Hutter, M., Legg, S., Veness, J., and Ortega, P. A. (2023). Neural networks and the chomsky hierarchy. In 11th International Conference on Learning Representations .

Deng, Y., Qiu, X., Tan, X., Qu, C., Pan, J., Cheng, Y., Xu, Y., and Chu, W. (2024). Cognidual framework: Self-training large language models within a dual-system theoretical framework for improving cognitive tasks. arXiv preprint arXiv:2409.03381 .

Dolan, R. J. and Dayan, P. (2013). Goals and habits in the brain. Neuron , 80(2):312-325.

Dumpala, S. H., Jaiswal, A., Shama Sastry, C., Milios, E., Oore, S., and Sajjad, H. (2024). Sugarcrepe++ dataset: Vision-language model sensitivity to semantic and lexical alterations. Advances in Neural Information Processing Systems , 37:17972-18018.

Echterhoff, J., Liu, Y ., Alessa, A., McAuley, J., and He, Z. (2024). Cognitive bias in decision-making with llms. In Findings of the Association for Computational Linguistics: EMNLP 2024 , pages 12640-12653.

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

Evans, J. S. B. and Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on psychological science , 8(3):223-241.

Furniturewala, S., Jandial, S., Java, A., Banerjee, P., Shahid, S., Bhatia, S., and Jaidka, K. (2024). Thinking fair and slow: On the efficacy of structured prompts for debiasing language models. arXiv preprint arXiv:2405.10431 .

Geva, M., Khashabi, D., Segal, E., Khot, T., Roth, D., and Berant, J. (2021). Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies. Transactions of the Association for Computational Linguistics , 9:346-361.

Gilardi, F., Alizadeh, M., and Kubli, M. (2023). Chatgpt outperforms crowd workers for textannotation tasks. Proceedings of the National Academy of Sciences , 120(30):e2305016120.

Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 .

Hagendorff, T., Fabi, S., and Kosinski, M. (2023). Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in chatgpt. Nature Computational Science , 3(10):833-838.

He, T., Liao, L., Cao, Y., Liu, Y., Liu, M., Chen, Z., and Qin, B. (2024). Planning like human: A dual-process framework for dialogue planning. In Ku, L.-W., Martins, A., and Srikumar, V., editors, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 4768-4791, Bangkok, Thailand. Association for Computational Linguistics.

Hosseini, M. J., Hajishirzi, H., Etzioni, O., and Kushman, N. (2014). Learning to solve arithmetic word problems with verb categorization. In Moschitti, A., Pang, B., and Daelemans, W., editors, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages 523-533, Doha, Qatar. Association for Computational Linguistics.

Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y ., Wang, S., Wang, L., and Chen, W. (2021). Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 .

Hua, W. and Zhang, Y. (2022). System 1 + system 2 = better world: Neural-symbolic chain of logic reasoning. In Goldberg, Y., Kozareva, Z., and Zhang, Y., editors, Findings of the Association for Computational Linguistics: EMNLP 2022 , pages 601-612, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Huang, J. and Chang, K. C.-C. (2022). Towards reasoning in large language models: A survey. arXiv preprint arXiv:2212.10403 .

Huang, J. and Chang, K. C.-C. (2023). Towards reasoning in large language models: A survey. In Rogers, A., Boyd-Graber, J., and Okazaki, N., editors, Findings of the Association for Computational Linguistics: ACL 2023 , pages 1049-1065, Toronto, Canada. Association for Computational Linguistics.

Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., and Han, J. (2022). Large language models can self-improve. arXiv preprint arXiv:2210.11610 .

Hurst, A., Lerer, A., Goucher, A. P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., Hayes, A., Radford, A., et al. (2024). Gpt-4o system card. arXiv preprint arXiv:2410.21276 .

Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L. R., Lachaux, M.-A., Stock, P., Scao, T. L., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E. (2023). Mistral 7b.

Jiang, B., Xie, Y ., Hao, Z., Wang, X., Mallick, T., Su, W. J., Taylor, C. J., and Roth, D. (2024). A peek into token bias: Large language models are not yet genuine reasoners. In Al-Onaizan, Y., Bansal, M., and Chen, Y.-N., editors, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages 4722-4756, Miami, Florida, USA. Association for Computational Linguistics.

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

Kahneman, D. (2011). Thinking, fast and slow . Farrar, Straus and Giroux, New York.

Kamruzzaman, M. and Kim, G. L. (2024). Prompting techniques for reducing social bias in llms through system 1 and system 2 cognitive processes. arXiv preprint arXiv:2404.17218 .

Keramati, M., Dezfouli, A., and Piray, P. (2011). Speed/accuracy trade-off between the habitual and the goal-directed processes. PLoS computational biology , 7(5):e1002055.

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y. (2022). Large language models are zero-shot reasoners. Advances in neural information processing systems , 35:22199-22213.

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y. (2023). Large language models are zero-shot reasoners.

Koncel-Kedziorski, R., Hajishirzi, H., Sabharwal, A., Etzioni, O., and Ang, S. D. (2015). Parsing algebraic word problems into equations. Transactions of the Association for Computational Linguistics , 3:585-597.

Kong, A., Zhao, S., Chen, H., Li, Q., Qin, Y., Sun, R., Zhou, X., Wang, E., and Dong, X. (2024). Better zero-shot reasoning with role-play prompting.

Lakoff, G. (1973). Hedges: A study in meaning criteria and the logic of fuzzy concepts. Journal of philosophical logic , 2(4):458-508.

Lee, S. W., Shimojo, S., and O'doherty, J. P. (2014). Neural computations underlying arbitration between model-based and model-free learning. Neuron , 81(3):687-699.

Li, H., Nourkhiz Mahjoub, H., Chalaki, B., Tadiparthi, V., Lee, K., Moradi Pari, E., Lewis, C., and Sycara, K. (2024). Language grounded multi-agent reinforcement learning with humaninterpretable communication. Advances in Neural Information Processing Systems , 37:8790887933.

Ling, W., Yogatama, D., Dyer, C., and Blunsom, P. (2017). Program induction by rationale generation: Learning to solve and explain algebraic word problems. In Barzilay, R. and Kan, M.-Y., editors, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 158-167, Vancouver, Canada. Association for Computational Linguistics.

Liu, Z., Wang, Z., Lin, Y., and Li, H. (2022). A neural-symbolic approach to natural language understanding. arXiv preprint arXiv:2203.10557 .

Magister, L. C., Mallinson, J., Adamek, J., Malmi, E., and Severyn, A. (2022). Teaching small language models to reason. arXiv preprint arXiv:2212.08410 .

Mattar, M. G. and Daw, N. D. (2018). Prioritized memory access explains planning and hippocampal replay. Nature neuroscience , 21(11):1609-1617.

Meng, Y., Xia, M., and Chen, D. (2024). Simpo: Simple preference optimization with a reference-free reward. arXiv preprint arXiv:2405.14734 .

Mirzadeh, I., Alizadeh, K., Shahrokhi, H., Tuzel, O., Bengio, S., and Farajtabar, M. (2024). Gsmsymbolic: Understanding the limitations of mathematical reasoning in large language models. arXiv preprint arXiv:2410.05229 .

Mondorf, P. and Plank, B. (2024). Beyond accuracy: Evaluating the reasoning behavior of large language models-a survey. arXiv preprint arXiv:2404.01869 .

Muennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candès, E., and Hashimoto, T. (2025). s1: Simple test-time scaling. arXiv preprint arXiv:2501.19393 .

Ott, D. E. (2018). Hedging, weasel words, and truthiness in scientific writing. JSLS: Journal of the Society of Laparoendoscopic Surgeons , 22(4).

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022). Training language models to follow instructions with human feedback. Advances in neural information processing systems , 35:27730-27744.

Pan, J., Zhang, Y., Zhang, C., Liu, Z., Wang, H., and Li, H. (2024). Dynathink: Fast or slow? a dynamic decision-making framework for large language models. arXiv preprint arXiv:2407.01009 .

Parmar, M., Patel, N., Varshney, N., Nakamura, M., Luo, M., Mashetty, S., Mitra, A., and Baral, C. (2024). Logicbench: Towards systematic evaluation of logical reasoning ability of large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 13679-13707.

Patel, A., Bhattamishra, S., and Goyal, N. (2021). Are NLP models really able to solve simple math word problems? In Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., and Zhou, Y., editors, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages 2080-2094, Online. Association for Computational Linguistics.

Piray, P. and Daw, N. D. (2021). Linear reinforcement learning in planning, grid fields, and cognitive control. Nature communications , 12(1):4942.

Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., and Finn, C. (2024). Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems , 36.

Roy, S. and Roth, D. (2015). Solving general arithmetic word problems. In Màrquez, L., CallisonBurch, C., and Su, J., editors, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing , pages 1743-1752, Lisbon, Portugal. Association for Computational Linguistics.

Sap, M., Rashkin, H., Chen, D., LeBras, R., and Choi, Y. (2019). Socialiqa: Commonsense reasoning about social interactions. arXiv preprint arXiv:1904.09728 .

Schad, D. J., Rapp, M. A., Garbusow, M., Nebe, S., Sebold, M., Obst, E., Sommer, C., Deserno, L., Rabovsky, M., Friedel, E., et al. (2020). Dissociating neural learning signals in human sign-and goal-trackers. Nature human behaviour , 4(2):201-214.

Singh, S., Wen, N., Hou, Y., Alipoormolabashi, P., Wu, T.-l., Ma, X., and Peng, N. (2021). COM2SENSE: A commonsense reasoning benchmark with complementary sentences. In Zong, C., Xia, F., Li, W., and Navigli, R., editors, Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , pages 883-898, Online. Association for Computational Linguistics.

Singhal, P., Goyal, T., Xu, J., and Durrett, G. (2023). A long way to go: Investigating length correlations in rlhf. ArXiv , abs/2310.03716.

Sourati, Z., Ilievski, F., Sommerauer, P., and Jiang, Y . (2024). Arn: Analogical reasoning on narratives. Transactions of the Association for Computational Linguistics , 12:1063-1086.

Stanovich, K. E. and West, R. F. (2000). Advancing the rationality debate. Behavioral and brain sciences , 23(5):701-717.

Talmor, A., Herzig, J., Lourie, N., and Berant, J. (2019). CommonsenseQA: A question answering challenge targeting commonsense knowledge. In Burstein, J., Doran, C., and Solorio, T., editors, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , pages 4149-4158, Minneapolis, Minnesota. Association for Computational Linguistics.

Tversky, A. and Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases: Biases in judgments reveal some heuristics of thinking under uncertainty. science , 185(4157):1124-1131.

Valmeekam, K., Olmo, A., Sreedharan, S., and Kambhampati, S. (2022). Large language models still can't plan (a benchmark for llms on planning and reasoning about change). In NeurIPS 2022 Foundation Models for Decision Making Workshop .

570

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613

614

615

616

617

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Wang, L., Xu, W., Lan, Y., Hu, Z., Lan, Y., Lee, R. K.-W., and Lim, E.-P. (2023). Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models. In Rogers, A., Boyd-Graber, J., and Okazaki, N., editors, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 2609-2634, Toronto, Canada. Association for Computational Linguistics.

Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D. (2022). Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171 .

Wang, X. and Zhou, D. (2024). Chain-of-thought reasoning without prompting. arXiv preprint arXiv:2402.10200 .

Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al. (2022a). Emergent abilities of large language models. arXiv preprint arXiv:2206.07682 .

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D. (2023a). Chain-of-thought prompting elicits reasoning in large language models.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al. (2022b). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems , 35:24824-24837.

Wei, X., Cui, X., Cheng, N., Wang, X., Zhang, X., Huang, S., Xie, P., Xu, J., Chen, Y., Zhang, M., Jiang, Y., and Han, W. (2023b). Zero-shot information extraction via chatting with chatgpt.

Weston, J. and Sukhbaatar, S. (2023). System 2 attention (is something you might need too). arXiv preprint arXiv:2311.11829 .

Xiao, M., Xie, Q., Kuang, Z., Liu, Z., Yang, K., Peng, M., Han, W., and Huang, J. (2024). HealMe: Harnessing cognitive reframing in large language models for psychotherapy. In Ku, L.-W., Martins, A., and Srikumar, V., editors, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 1707-1725, Bangkok, Thailand. Association for Computational Linguistics.

Xu, C., Sun, Q., Zheng, K., Geng, X., Zhao, P., Feng, J., Tao, C., and Jiang, D. (2023). Wizardlm: Empowering large language models to follow complex instructions. arXiv preprint arXiv:2304.12244 .

Xu, H. (2021). Career decision-making from a dual-process perspective: Looking back, looking forward. Journal of Vocational Behavior , 126:103556.

Yang, C., Shi, C., Li, S., Shui, B., Yang, Y ., and Lam, W. (2024). Llm2: Let large language models harness system 2 reasoning. arXiv preprint arXiv:2412.20372 .

Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., and Narasimhan, K. (2024). Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems , 36.

Yu, P., Xu, J., Weston, J., and Kulikov, I. (2024). Distilling system 2 into system 1. arXiv preprint arXiv:2407.06023 .

Zeng, Z., Liu, Y., Wan, Y., Li, J., Chen, P., Dai, J., Yao, Y ., Xu, R., Qi, Z., Zhao, W., et al. (2024). Mrben: A meta-reasoning benchmark for evaluating system-2 thinking in llms. In The Thirty-eighth Annual Conference on Neural Information Processing Systems .

Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al. (2023). Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems , 36:46595-46623.

Zhou, H., Qian, J., Feng, Z., Hui, L., Zhu, Z., and Mao, K. (2024a). LLMs learn task heuristics from demonstrations: A heuristic-driven prompting strategy for document-level event argument extraction. In Ku, L.-W., Martins, A., and Srikumar, V., editors, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 1197211990, Bangkok, Thailand. Association for Computational Linguistics.

Zhou, P., Pujara, J., Ren, X., Chen, X., Cheng, H.-T., Le, Q. V., Chi, E. H., Zhou, D., Mishra, S., and 619 Zheng, H. S. (2024b). Self-discover: Large language models self-compose reasoning structures. 620 arXiv preprint arXiv:2402.03620 . 621

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A Limitations

Despite the promising advancements of using different thinking styles through the lens of dual-process cognitive theory in our approach, it is important to clarify the intended scope and outline future directions. Our curated dataset of 2,000 questions covers 10 well-established cognitive heuristics and was validated by our domain experts to ensure quality. While not exhaustive, this dataset provides a strong foundation for investigating reasoning style differences and establishes methodological groundwork for broader-scale expansion in future studies to represent the entire spectrum of reasoning challenges encountered in real-world tasks. We focused our alignment experiments on Llama and Mistral as base models, using DPO and SIMPO as preference optimization techniques. While our findings are likely to generalize across model architectures and alignment methods, given the shared emergence of both intuitive and deliberative reasoning in large-scale pretraining, testing this generalization to other architectures and alignment methods is a valuable future direction. In terms of evaluating reasoning uncertainty, we adopt token-level logit-based measures and linguistic hedging analysis as computationally tractable proxies. These provide interpretable signals of reasoning behavior, though deeper psycholinguistic and interactive evaluations may offer complementary insights. Finally, while our experiments reveal a clear accuracy-efficiency trade-off between intuitive and deliberative reasoning, the extent to which these findings translate to more complex or dynamic decision-making scenarios remains an open question. Future work should explore larger, more diverse datasets and investigate alternative alignment strategies to further validate and extend these results.

B Ethical Statement

Aligning LLMs with System 1 and System 2 reasoning raises concerns about model behavior in different contexts. System 1 models may produce overly confident but incorrect responses, while System 2 models, though more deliberate, may slow response times and increase computational costs. Responsible deployment requires balancing these trade-offs to prevent biased or misleading outputs.

C Cognitive heuristics

In Table 3, we list 10 different cognitive heuristics and their definitions, which we used in curating 647 the dataset Kahneman (2011); Stanovich and West (2000); Evans and Stanovich (2013).

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D Details of Experts

The experts consulted are the two authors of this paper, both of whom are Ph.D. students in Psychology 650 with a focus on cognitive and social science. 651

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E Initial Data Examples

The 10 samples generated by the expert for our data generation are shown in Table 4. 653

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F Prompt for Data Expansion

We expand our sample dataset by concatenating the expert-generated samples with the definitions in 655 Table 3, along with a description of how System 1 and System 2 would respond to a given question, 656 as shown below: 657

The System 1 response should be intuitive, fast, and reflect the cognitive heuristic associated with the question.

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The System 2 response should be more deliberate, slower, and use reasoning to correct or mitigate the heuristic.

G Topic Modeling

Following expert validation, we experimentally verified the diversity of our dataset to ensure it goes 661 beyond surface-level variation in wording. Figure 5 presents the results of topic modeling using 662 BERTopic (Grootendorst, 2022), demonstrating the range of topics covered in the dataset. The wide 663 distribution and clustering across 150 unique topics demonstrate the semantic diversity of the dataset 664 beyond superficial lexical variation. 665

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4_price_auction_see

5_investment_risky_investing

6_team_recent_sports

7_fashion_wearing_buy

12_restaurant_reviews_restaurants

21_pass_exam_studying

22_online_course_join

24_celebrity_rumor_whats

26_estimate_budget_cost

2_person_again_not

3_ob_interview_role

xpis"5uAy"ng“6

10_project_deadlines_missing

11_reading_book_finish

13_holiday_during_accidents

14_meeting_meetings_slides

15_organizing_fles_security

16_diet_effective_meals

18_been_tomorrow_have

19_decision_deal_financial

20_health_ifestyle_track

25_practicing_win_match

27_reliable_firm_trust

29 hurricanes earthouakes storms

0_political_politician_candidate

28_product_unsafe_car

33_promotion_responsibilities_receivin

34_insurance_provider_healthcare

35_dangerous_alone_hiking

36_lottery_win_winning

37_skincare_brand_code

38_fitness_trainer_Joining

Sugaew"am"no6C

41_housing_city_house

42_preject_theory_waste

46_product_market_packaging

49_startup_tech_startups

50_app_downloading_software

51_recover_medication_treatment

53_comment_someones_someone

55_trend_scam_convinced

56_editing_finish_by

58_movie_watching_positive

59_fumiture_assemble_arrive

30_phone_model_headphones

31_product_does_quality

32_clothing_fashion_ethical

40_presentation_preparing_conference

43_take_long_install

44_offer_negotiations_hard

45_proposal_paperwork_write

47_event_boring_poorly

48_see_movie_whether

52_apartment_appliances_fire

54_report_proofread_annual

57_tv_show_podcast

72_marketing_target_sales

61_dangerous_amusement_theme

62_food_fast_poisoning

65_trust_dealership_dishonest

67_training_marathon_race

68_strategy_work_method

duAaoapedxaasje69

71_strict_weight_fllowing

73_drinking_coffee_tea

74_provider_switching_emall

75_restaurant_order_choose

77_fitness_join_workout

ppapuwalddns6

80_plan_carefully_unrealistic

81_gaming_buy_gadget

82_course_dificult_class

83_cooking_cook_meal

84_companys_sucoess_earmings

85_feedback_critical_performance

86_traffic_delays_stuck

87_employees_we_introduce

88_influencer_media_social

swalgodasnesAogod68

60_bank_card_switch

63_fix_problem_technical

64_workout_exercise_routine

66_assignment_essay_grading

70_packing_trip_before

76_remember_notes_details

78_vacation_pick_trall

92_report_completed_finished

96_relationship_issues_couples

98_medical_advice_recommendation

99_hobby_continuing_committed

102_goals_savings_cutting

104_downturn_economy_economic

106_invest_cryptocurrency_startup

109_customer_service_company

110_shoes_brands_style

111_meal_enjoying_dish

112_launch_ready_launching

115_skil_leaming_developing

116_learm_play_language

sueo“pjo“suappoe“06

xy“s5nq“asqam"16

93_stock_rise_company

94_argument_resolve_conflict

95_talented_photographer_artists

97_business_plan_succeed

100_metorcycles_stolen_cars

101_balance_worklife_life

103_book_series_books

105_finalize_long_take

107_streaming_service_sign

108_they_mood_performing

113_impression_rely_later

114_scammed_cyberattacks_often

117_organizing_tasks_method

118_book_authors_reading

119_violent_shootings_crimes

124_delays_timeline_project

125_business_streams_explore

128_transportation_travel_route

135_map_navigate_unfemiliar

136_geins_growing_stock

138_advisor_financial_describe

143_argument_strong_debate

144_grocery_shopping_delivery

148_person_incompetent_urreliable

121_technology_breakthrough_overhyped

veu"wuogs"Auuns"c2t

125_an_bias_interpret

127_sel_discounts_off

130_lient_land_needs

131_vacations_vacation_stressful

132_rehearsing_karaoke_though

133_upgrade_software_done

134_work_taking_break

137_income_badkup_case

wuaaa/"ases“fues"6(1

140_topic_article_read

141_toothpaste_brand_laundry

142_preductive_late_temorrew

145_heinsty/le_haircut_beard

146_salary_expectation_number

147_forecast_orders_expectations

120_shark_swimming_swim

122_decisions_investment_market

H Benchmark Details

We use three categories of reasoning benchmarks: arithmetic, commonsense reasoning, symbolic reasoning, We provide an overview of the datasets used in each category.

Arithmetic reasoning. We use six datasets: MultiArith, GSM8K, AddSub, AQuA, SingleEq, and SVAMP. Each dataset consists of questions that present a scenario requiring numerical computation and multi-step reasoning based on mathematical principles.

Commonsense reasoning. To assess commonsense reasoning, we utilize five benchmarks: CommonsenseQA (CSQA), StrategyQA, PIQA, SocialIQA (SIQA), and Com2Sense. All require models to go beyond surface-level understanding and reason using prior knowledge. CSQA focuses on multiple-choice questions grounded in general world knowledge, while StrategyQA includes questions that demand implicit multi-hop reasoning. PIQA evaluates physical commonsense by requiring models to choose the more plausible solution to everyday benchmarks. SIQA targets social commonsense, presenting scenarios about interpersonal interactions and asking questions about motivations, reactions, and emotions. Com2Sense provides pairs of complementary sentences to test a model's ability to distinguish between plausible and implausible statements using commonsense.

Symbolic reasoning. We use the Last Letter Concatenation and Coin Flip datasets. Last Letter Concatenation involves forming a word by extracting the last letter of given words in order. Coin Flip presents a sequence of coin-flipping instructions and asks for the final coin orientation. These datasets were originally proposed by Wei et al. (2023a) but were not publicly available. Kojima et al. (2023) later followed their approach to create and release accessible versions, which we use in our experiments.

I Equivalence Testing of Dataset Lengths Using TOST

A two one-sided t-test (TOST) confirmed the equivalence of these post-adjustment lengths across various token counts as equivalence margins: ± 3 tokens, t (3870 . 30) = 85 . 82 , p < . 001 ; ± 5 tokens, t (3870 . 30) = 149 . 07 , p < . 001 ; ± 7 tokens, t (3870 . 30) = 212 . 31 , p < . 001 ; and 5% of the mean token count ( ± 4 . 15 tokens), t (3870 . 30) = 122 . 29 , p < . 001

J Length Adjustment Threshold and Prompt

We adjust the length if there is a disparity of more than 15 tokens between the System 1 and System 693 2 outputs using GPT-4o with the following prompt: 694

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K Benchmark Instruction 696

The benchmark-specific instructions are shown in Table 5.

L Implementation Details

We use Python 3.10.12, PEFT 0.12.0, PyTorch 2.4.0, and Transformers 4.44.2. The dataset is split into 80% training and 20% validation. For alignment, we apply Low-Rank Adaptation (LoRA Hu et al., 2021) with a rank of 8, an alpha of 16, and dropout rate of 0.1. We train for five epochs, using accuracy on winner responses as an early stopping criterion to prevent overfitting, with patience of 5. We set the train batch size to 4 and the validation batch size to 8. To align Llama 3 using the DPO method, we followed Meng et al. (2024) and set the learning rate to 7 e -7 with beta of 0 . 01 . For SimPO, we use a learning rate of 1 e -6 , beta of 2 . 5 , and a gamma-to-beta ratio of 0 . 55 . For Mistral v0.1, we set the DPO learning rate to 5 e -7 with beta of 0 . 001 . In SimPO, we use a learning rate of 5 e -7 , beta of 2 . 5 , and a gamma-to-beta ratio of 0 . 1 .

The experiments were conducted using NVIDIA RTX A6000 GPU equipped with 48GB of RAM.

The total computation time amounted to approximately 800 GPU hours.

M Moving from Fast to Slow Thinking Plots

Figure 6 demonstrates a consistent, monotonic increase in accuracy across all other benchmarks.

N Additional Insights into Models' Reasoning

In this analysis, we investigate when different models reach definitive answers. We aim to detect this commitment as early as possible during the reasoning process. This early commitment serves as a proxy for the model's confidence in the generated reasoning and its final answer. By analyzing this behavior, we explore whether models can arrive at a definitive answer or if they leave room for ambiguity or subjective interpretation.

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Your task is to adjust the two answers so that they are presented in the same order of tokens without altering their content. Ensure that the intuitive nature of the System 1 Answer and the logical reasoning of the System 2 Answer are preserved.

AddSub (Arithmetic)

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PIQA (Common Sense)

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We leverage the strong extractive capabilities of LLMs (Wei et al., 2023b) and their near-human-like 718 annotation abilities (Gilardi et al., 2023; Alizadeh et al., 2023). Specifically, we focus on the Phi4 719 (14B) model (Abdin et al., 2024), which demonstrates exceptional performance in question-answering 720 and reasoning benchmarks, even surpassing closed-source models like GPT-4o (Hurst et al., 2024). 721 To determine whether a model's reasoning contains a definitive answer, we use the following prompt 722 fed to Phi4: 723

Does the given answer directly answer the given question in a definitive way? ONLY RETURN YES OR NO IN A \textbf{}. Definitive answers are clear and do not leave room for interpretation or ambiguity. If the answer tries to explore multiple perspectives or factors involved, it is not definitive, and YOU HAVE TO RETURN NO.

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This prompt is applied to reasoning generated by both System 1 and System 2 models. To understand when these models commit to a definitive answer during their reasoning process, we focus on the first n sentences of their reasoning, where n ∈ { 1 , 3 , 6 , 9 , 12 , 15 } . We set a cap of 15 sentences based on our observations that nearly all generated reasonings across benchmarks fall within this range (see Figure 8).

Applying the prompt to each generated reasoning from the models across all benchmarks (200 randomly sampled data points from each benchmark, totaling 2000 samples for both System 1 and System 2 reasonings), we append six solved demonstrations to the prompt to help further guide the models. These demonstrations, selected randomly from the cognitive heuristics introduced in Section 3.2, help clarify what qualifies as a definitive answer, aligning the models' knowledge with patterns we have aligned System 1 and 2 models with (see Section 3.1).

Figure 7 shows the proportion of definitive answers in the first n sentences, across all benchmarks. 2 For tasks where quick, intuitive judgments are advantageous, such as in commonsense reasoning. System 1 models consistently provide more definitive answers than System 2 models. This gap emerges early, with System 1 providing more definitive answers in the first three sentences. The difference persists even as we extend the number of sentences considered (see Table 6 for a quantitative analysis of the significance between System 1 and System 2 regarding the definitiveness of their answers).

O System-Specific Failure Patterns

To complement the main results, we include two analyses that illustrate how System 1 and System 2 744 models diverge in failure patterns depending on task type. In numerical reasoning benchmarks, System 745 2 models are more reliable when higher precision is required, while in commonsense benchmarks, 746 System 1 models tend to produce more contextually appropriate answers. The following figure and 747 table offer additional insight into these differences. 748

To further analyze the behavioral differences between System 1 and System 2 models, we examine 749 their performance on AddSub items with varying numeric complexity. Figure 9 shows the distribution 750 of digit types in ground truth answers across four outcome categories. Notably, in examples where 751 System 2 succeeds and System 1 fails ('Sys2 better'), the ground truth answers tend to have a 752

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significantly higher number of floating-point digits (Mann-Whitney U test, U = 346 . 0 , p = 0 . 0051 ). 753 This pattern suggests that System 2 is more effective at handling cases requiring greater numerical 754 precision. In contrast, the number of total digits (irrespective of decimal placement) does not differ 755 meaningfully between the 'Sys2 better' and 'Sys1 better' subsets ( U = 224 . 0 , p = 0 . 99 ). 756

We also provide a qualitative comparison of commonsense failures made by System 2, shown in 757 Table 7. The table includes representative examples from CSQA where System 2 responses, although 758 logically coherent, miss intuitive or socially grounded answers. These cases highlight how interpretive 759 depth can lead to answers that diverge from typical human judgment. 760

P Technical Appendices and Supplementary Material 761

Technical appendices with additional results, figures, graphs and proofs may be submitted with 762 the paper submission before the full submission deadline (see above), or as a separate PDF in the 763 ZIP file below before the supplementary material deadline. There is no page limit for the technical 764 appendices. 765

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NeurIPS Paper Checklist

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785

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5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [Yes]

Justification: We have open-sourced our code as well as the accompanying dataset that is introduced in the paper.

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The answer NA means that paper does not include experiments requiring code.

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The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: All experimental settings and details are provided in Section 4.3 and Appendix L.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [Yes]

Justification: We used suitable statistical analysis based on the variable in different part of our paper such as Section 3.2, Section 5.3, Section 5.2, and Section 5.4.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: In Appendix L, we have provided the computation resources.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: We have read the NeurIPS Code of Ethics and made sure that the paper conforms to it.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: We provide high-level, abstract interpretations of our results and discuss their broader implications, including potential societal impacts, in both Section 5 and Section 6. These include the risks of over-relying on a single mode of reasoning in LLMs and the importance of aligning model behavior with context-sensitive cognitive strategies.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

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Justification: This paper contributes to the field of reasoning in language models, which is neutral with respect to misuse or dual-use concerns. It does not involve the release of highrisk models or datasets and does not pose foreseeable risks requiring specific safeguards.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: We used Apache-2.0 as our license in github.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [Yes]

Justification: Our code and data are available on GitHub and Hugging Face.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

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14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA] .

Justification: Our paper does not involve crowdsourcing nor research with human subjects. Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA] .

Justification: Our paper did not involve crowdsourcing and human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

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16. Declaration of LLM usage

Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [NA] .

Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components.

Guidelines:

The answer NA means that our core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper uses 2K LLM-synthesised data to align SFT models with System 1 (fast, intuitive) and System 2 (slow, analytical) thinking separately. With the resulting \"System 1 models\" and \"System 2 models\", the paper reports their performance differences across 13 reasoning benchmarks and argues the task specific performance divergence. \nThe paper also claims that System 2-aligned models generate longer responses than System 1-aligned models, even when trained on equal-length preference pairs. Interpolating between System 1 and System 2 alignment ratios reveals a monotonic transition in accuracy.\nSystem 2 models exhibit higher uncertainty, while System 1 models provide more definitive responses early in their reasoning. Overall the paper challenging the \"Step-by-Step Always Optimal\" assumption.\n\nSTRENGTHS AND WEAKNESSES:\nStrenghts:\nThe paper is well organized and proficiently written, with no obvious errors.\nThe experiments are easily reproducible.\nThe cognitive science behind is well acknowledged.\nThe related work section is satisfying.\n\nWeaknesses:\nThe data size is modest, the data is LLM generated, potentially limiting coverage of real-world reasoning complexity.\nAligns only with Llama and Mistral; results may not generalize to other well-studied and recognized LLMs (e.g., GPT-family models, Qwen series, Deepseek).\nAssumes System 1/2 as discrete endpoints, but human reasoning often involves parallel processing rather than strict sequential switching. The paper’s linear interpolation may oversimplify cognitive dynamics.\nIt lacks details of the experiment setups, like how do the system 1/2 models comparison following the rules of contrast experiments\n\nQUESTIONS:\n1. Is the effectiveness of the alignment training verifiable? Is there a justifiable basis to claim that training on system 1 thinking data of your system can transform an SFT model into a \"system 1 model\", and that training on system 2 thinking data can make it a \"system 2 model\"?\n2. System 1 and system 2 thinking are typically distinct strategies for addressing different problems. Is it not arduous to curate both system 1 and system 2 thinking for the same question and compel the model to conform to them? I question the effectiveness and practicality of the alignment experiments. As we know there are new reasoning models that can conduct both thinking and direct answers (Qwen3, Claude 3.7) in the industry, how is your method compared to the industry practices? Are you following the path of these industry models?\n3. You present numerous claims that are generally heuristically recognized by the community, yet they lack robust evidence. In the section \"Length Differences Across Reasoning Styles\", you conclude that “extended reasoning is not universally beneficial” without conducting experiments and statistical analyses to substantiate this claim. In fact, not every experiment in your paper necessarily supports your conclusion. As mentioned above, I have reservations about the \"system 1/2 model\", as well as the interpolated models. Additionally, I would like to see more elaborate details regarding the uncertainty experiments. \n4. Recommended citation: Break the chain: Large language models can be shortcut reasoners\n\nLIMITATIONS:\nOversimplified theoretical framework\nModest data scale and synthetic nature\nGaps in experimental design and validation", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis submission presents a framework for examining and influencing the reasoning of Large Language Models (LLMs) utilizing dual-process theory from cognitive science. The authors compile a dataset comprising 2,000 questions, each accompanied by two legitimate responses that reflect rapid, intuitive \"System 1\" cognition and deliberate, analytical \"System 2\" reasoning. Utilizing this dataset, they adjust LLMs (Llama-3 and Mistral-7B) to each reasoning style via preference optimization techniques (DPO and SimPO). The submission further analyzes the characteristics of these models, demonstrating that System 2 reasoning is associated with longer, more uncertain responses, while System 1 is more token-efficient and definitive.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n- The submission looks at accuracy in more than just surface-level ways.  By using interpolation to look at the reasoning spectrum, the trade-offs in token efficiency, and the study of model uncertainty (through logits, hedge words, and response definitiveness), we can learn a lot about how the alignment process changes behavior.\n- The submission tests 13 different benchmarks using a variety of models, such as Llama-3 and Mistral-7B, as well as alignment methods like DPO and SimPO.  These benchmarks include tasks that require arithmetic, common sense, and symbolic reasoning, which makes the differences between System 1 and System 2 reasoning clear and reliable.\n- The paper is well-organized.  And it's easy to see the difference between LLM step-by-step reasoning and System 1/2 thinking.\n\n**Weaknesses:**\n\n- The submission’s core methods primarily integrate existing preference optimization techniques (e.g., DPO, SimPO) and dual-process cognitive theories without proposing algorithmic frameworks. The strategies for organizing and aligning the dataset are systematic; however, the overall technical approach depends on the integration of existing methods, suggesting a deficiency in innovative methodological advancements that could improve its algorithmic contribution.  \n- While the submission adeptly demonstrates the existence of a reasoning trade-off, it does not provide a mechanism or discussion for a model to dynamically select the appropriate reasoning style for a particular task.  This constrains the immediate practical effect of the work. \n- The validity of certain \"System 1\" examples in the dataset may be scrutinized. In some cases, the answers seem to be more like commentaries on how to use intuition (for example, \"My gut instinct is usually right\" in Table 10, Appendix E) than direct, intuitive outputs. This could make the model articulate System 1 thinking instead of executing it, which is a subtle but important difference.\n- The analysis of uncertainty serves as a preliminary step in this investigation, yet the primary emphasis is on aligning the textual output.  The issue of whether the model's foundational computational mechanism fundamentally alters remains predominantly unexamined.  More in-depth investigations (e.g., examining internal activations) are required to validate the assertion of aligning cognitive processes.\n\nQUESTIONS:\n1. Have you done any early tests to make a \"router\" model that picks between the System 1 and System 2 experts based on a question?  Or maybe a single, larger model could be made to choose its own mode of operation?\n2. Is it possible to look at the models' internal states to support your claim that their \"thinking\" styles are similar?  For example, do attention patterns or activation vectors show consistent differences between the System 1 and System 2 models when they are processing the same input?\n3. Does the conclusion from this submission, such as \"System 2 models exhibit greater uncertainty throughout the reasoning process, potentially enabling them to engage in more structured, step-by-step problem-solving,\" also apply to the reasoning model, such as QwQ-32B?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel framework for aligning large language models (LLMs) with human-like reasoning styles inspired by dual-process theories from cognitive psychology—namely, System 1 (fast, intuitive, heuristic) and System 2 (slow, analytical, deliberate) thinking. The authors construct a carefully curated dataset of 2,000 questions, each paired with both System 1 and System 2 style answers, grounded in ten canonical cognitive heuristics. Using preference-based fine-tuning (DPO and SimPO), they train LLMs to preferentially adopt one reasoning style over the other. The paper empirically demonstrates that System 2-aligned models perform better on structured, multi-step tasks (e.g., arithmetic), while System 1-aligned models excel in commonsense reasoning tasks, offering faster and more intuitive responses. The authors also analyze reasoning behaviors (length, certainty, hedge usage), revealing smooth interpolations along the reasoning spectrum and highlighting a trade-off between accuracy and efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- **Quality**: The experimental design is rigorous, well-controlled, and grounded in both machine learning methodology and cognitive science theory. The authors take great care to control for confounding factors such as response length and structure.\n- **Clarity**: The paper is well-written and clearly structured. Key concepts—such as dual-process theory, preference optimization, and reasoning trade-offs—are introduced with sufficient background for both NLP and cognitive science audiences.\n- **Originality**: This work represents a unique interdisciplinary contribution, bridging cognitive psychology and LLM training in a way that is rarely explored. Explicitly aligning LLMs with different cognitive styles—rather than assuming step-by-step reasoning is always superior—is a fresh and valuable angle.\n- **Significance**: The findings challenge prevailing assumptions in LLM research (e.g., “more reasoning steps is always better”) and could influence future directions in model alignment, especially for applications involving user interaction, commonsense, or time-sensitive tasks.\n\nWeaknesses:\n\n- **Interpretability risks**: While System 1 alignment improves commonsense benchmarks, it also reinforces cognitive biases. This introduces a tension between performance and cognitive reliability that is not fully resolved, and risks being misinterpreted or misapplied by future practitioners. (See the Questions section for a detailed discussion)\n\nQUESTIONS:\nThe most important question I have concerns a potential **conceptual and ethical tension** raised by the findings:\n\n> Your results show that aligning LLMs to System 1 reasoning—characterized by stronger cognitive biases—leads to better performance on commonsense reasoning benchmarks. This suggests that injecting heuristic, bias-prone reasoning may improve accuracy in some tasks.\n> \n\nThis leads to two key concerns:\n\n1. **Interpretability of the Result**: Could the authors provide more clarification on *why* stronger cognitive biases appear to help on commonsense tasks? Do these benchmarks inherently reward human-like biases? If so, to what extent is this a desirable signal versus an artifact of benchmark design?\n2. **Risk of Misuse or Misinterpretation**: There is a risk that future work may interpret this as justification to intentionally align models toward biased behavior to improve perceived \"commonsense\" performance. How do the authors recommend mitigating this risk, especially when such biases may be harmful in real-world applications (e.g., stereotype amplification, unfair judgments)?\n\nLIMITATIONS:\n**Yes.**\n\nThe authors provide a comprehensive and thoughtful discussion of their work’s limitations. They explicitly address the behavioral side effects of aligning models to either System 1 or System 2 reasoning styles—including verbosity, uncertainty, and potential heuristic bias—and analyze these effects quantitatively (Section 5.4). They also acknowledge the limitations of their dataset scale and the specificity of their benchmark-driven evaluation, noting that results may not fully generalize to interactive or real-world scenarios. Furthermore, the paper’s conclusion and ethical impact section (Section 6, Broader Impacts) reflect a clear awareness of the societal risks associated with over-relying on a single reasoning style, particularly System 1, in contexts requiring precision and fairness.\n\nGiven these reflections, I believe the authors have been appropriately transparent, and there are no major omissions in their self-assessment.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper explores aligning LLMs with two types of human reasoning: fast, intuitive (System 1) and slow, analytical (System 2). Using a dataset of 2,000 examples reflecting both styles, the study shows that System 2 models perform better on arithmetic tasks, while System 1 models excel in commonsense reasoning. The results highlight the trade-off between accuracy and efficiency and suggest that reasoning strategies should adapt to task requirements.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n* This paper presents an interesting perspective by linking problem types to different reasoning styles (System 1 and System 2).\n* The experimental results effectively support the proposed concept.\n* Extensive analyses are provided, offering intuitive insights into model behavior.\n\nWeaknesses:\n* The connection between the benchmark categories and the corresponding reasoning styles (System 1 vs. System 2), as claimed by the author, appears insufficiently substantiated and requires further clarification. This should include a clear justification for why each type of benchmark aligns with a particular type of reasoning.\n* The results seem somewhat unsurprising, as models trained to prefer a particular reasoning style are naturally more aligned with tasks that reflect that same style.\n* In the generated dataset, both the questions and the System 1 and System 2 responses seem more like generalized descriptions of expected behavior for each reasoning type, rather than grounded, context-specific interactions. For instance, the question and the System 1 answer in the first row of Table 1 read more like an explanation of how that system typically behaves, rather than a realistic question paired with a natural, situated answer.\n* A more thorough qualitative evaluation (e.g., output examples) is needed to clearly illustrate how the System 1 and System 2 models behave across different benchmark types. This would help verify whether the models’ performance improves in the intended manner.\n\nQUESTIONS:\n- Why is commonsense reasoning assumed to require rapid and intuitive judgments?\n- Do the selected benchmarks faithfully reflect those characteristics?\n- Are the models separately trained on System 1 and System 2 reasoning types and then applied to the corresponding benchmarks?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}