S1-Bench: A Simple Benchmark for Evaluating System 1 Thinking Capability of Large Reasoning Models

• URL: http://arxiv.org/abs/2504.10368v1
• Date: Mon, 14 Apr 2025 16:13:23 GMT
• Title: S1-Bench: A Simple Benchmark for Evaluating System 1 Thinking Capability of Large Reasoning Models
• Authors: Wenyuan Zhang, Shuaiyi Nie, Xinghua Zhang, Zefeng Zhang, Tingwen Liu,
• Abstract summary: Large Reasoning Models' (LRMs) reliance on deep analytical thinking may limit their system 1 thinking capabilities.<n>We introduce S1-Bench, a novel benchmark designed to evaluate LRMs' performance on simple tasks.
• Score: 13.083179473480705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce S1-Bench, a novel benchmark designed to evaluate Large Reasoning Models' (LRMs) performance on simple tasks that favor intuitive system 1 thinking rather than deliberative system 2 reasoning. While LRMs have achieved significant breakthroughs in complex reasoning tasks through explicit chains of thought, their reliance on deep analytical thinking may limit their system 1 thinking capabilities. Moreover, a lack of benchmark currently exists to evaluate LRMs' performance in tasks that require such capabilities. To fill this gap, S1-Bench presents a set of simple, diverse, and naturally clear questions across multiple domains and languages, specifically designed to assess LRMs' performance in such tasks. Our comprehensive evaluation of 22 LRMs reveals significant lower efficiency tendencies, with outputs averaging 15.5 times longer than those of traditional small LLMs. Additionally, LRMs often identify correct answers early but continue unnecessary deliberation, with some models even producing numerous errors. These findings highlight the rigid reasoning patterns of current LRMs and underscore the substantial development needed to achieve balanced dual-system thinking capabilities that can adapt appropriately to task complexity.

Related papers

• a1: Steep Test-time Scaling Law via Environment Augmented Generation [45.19240207975418]
  Environment Augmented Generation (EAG) is a framework that enhances large language models' reasoning through real-time environmental feedback. EAG enables deliberate backtracking and strategic replanning through tight integration of execution feedback and branching exploration. A1-32B model achieves state-of-the-art performance among similar-sized models across all benchmarks.
  arXiv  Detail & Related papers  (2025-04-20T12:55:59Z)
• Trade-offs in Large Reasoning Models: An Empirical Analysis of Deliberative and Adaptive Reasoning over Foundational Capabilities [101.77467538102924]
  Recent advancements in Large Reasoning Models (LRMs) have demonstrated remarkable performance in specialized reasoning tasks.<n>We show that acquiring deliberative reasoning capabilities significantly reduces the foundational capabilities of LRMs.<n>We demonstrate that adaptive reasoning -- employing modes like Zero-Thinking, Less-Thinking, and Summary-Thinking -- can effectively alleviate these drawbacks.
  arXiv  Detail & Related papers  (2025-03-23T08:18:51Z)
• Large Reasoning Models in Agent Scenarios: Exploring the Necessity of Reasoning Capabilities [74.35956310688164]
  We propose the LaRMA framework, encompassing nine tasks across Tool Usage, Plan Design, and Problem Solving. Our findings address four research questions: LRMs surpass LLMs in reasoning-intensive tasks like Plan Design, leveraging iterative reflection for superior outcomes. LRMs' enhanced reasoning incurs higher computational costs, prolonged processing, and behavioral challenges, including overthinking and fact-ignoring tendencies.
  arXiv  Detail & Related papers  (2025-03-14T04:34:31Z)
• LR$^2$Bench: Evaluating Long-chain Reflective Reasoning Capabilities of Large Language Models via Constraint Satisfaction Problems [7.379503137362718]
  We introduce LR$2$Bench, a novel benchmark designed to evaluate the Long-chain Reflective Reasoning capabilities of Large Language Models (LLMs)<n>Our experimental results reveal that even the most advanced reasoning-specific models, such as DeepSeek-R1 and OpenAI o1-preview, struggle with tasks in LR$2$Bench.
  arXiv  Detail & Related papers  (2025-02-25T04:51:17Z)
• Reward Models Identify Consistency, Not Causality [54.987590763737145]
  State-of-the-art reward models prioritize structural consistency over causal correctness. Removing the problem statement has minimal impact on reward scores. altering numerical values or disrupting the reasoning flow significantly affects RM outputs.
  arXiv  Detail & Related papers  (2025-02-20T14:57:14Z)
• Reasoning on a Spectrum: Aligning LLMs to System 1 and System 2 Thinking [0.9709444454602557]
  Large Language Models (LLMs) exhibit impressive reasoning abilities, yet their reliance on structured step-by-step reasoning reveals a critical limitation.<n>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.
  arXiv  Detail & Related papers  (2025-02-18T02:58:37Z)
• MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency [63.23935582919081]
  Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs) We introduce MME-CoT, a specialized benchmark evaluating the CoT reasoning performance of LMMs. We conduct an in-depth analysis of state-of-the-art LMMs, uncovering several key insights.
  arXiv  Detail & Related papers  (2025-02-13T18:59:46Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)
• Exploring Self-supervised Logic-enhanced Training for Large Language Models [59.227222647741094]
  In this paper, we make the first attempt to investigate the feasibility of incorporating logical knowledge through self-supervised post-training. We devise an auto-regressive objective variant of MERIt and integrate it with two LLM series, i.e., FLAN-T5 and LLaMA, with parameter size ranging from 3 billion to 13 billion. The results on two challenging logical reasoning benchmarks demonstrate the effectiveness of LogicLLM.
  arXiv  Detail & Related papers  (2023-05-23T06:13:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.