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arxiv: 2509.20868 · v2 · submitted 2025-09-25 · 💻 cs.LG · cs.AI· cs.CL

StyleBench: Evaluating thinking styles in Large Language Models

Junyu Guo , Shangding Gu , Ming Jin , Costas Spanos , Javad Lavaei This is my paper

Pith reviewed 2026-05-18 14:36 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CL
keywords reasoning styleslarge language modelsbenchmarkchain of thoughtmodel capacityadaptive controlefficiency
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The pith

Greater reasoning structure in LLMs improves accuracy only in limited regimes set by task demands and model capacity.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

StyleBench treats reasoning structure as a capacity-constrained design choice rather than a fixed recipe and tests five styles across five tasks and fifteen models from 270M to 120B parameters. The central finding is that added structural complexity raises accuracy only when task demands and model size align, with search-based styles succeeding on open combinatorial problems yet collapsing on smaller models. Concise styles deliver large efficiency gains on structured tasks while preserving accuracy. Smaller models exhibit systematic failures such as premature guessing and poor adherence to control instructions. Supervised fine-tuning for style selection collapses to shallow preferences, whereas reinforcement-based methods like GRPO learn stronger adaptive control and lift downstream performance.

Core claim

We find that greater structural complexity improves accuracy only in limited regimes defined by task demands and model capacity. Search-based styles help on open-ended combinatorial problems but fail on smaller models, while concise styles achieve large efficiency gains on structured tasks without sacrificing performance. We also identify systematic failure modes in smaller models, including premature guessing and weak adherence to reasoning-control instructions. Supervised fine-tuning collapses to shallow style preferences, whereas GRPO learns stronger adaptive control and improves downstream performance.

What carries the argument

StyleBench, a benchmark that evaluates five representative reasoning styles (Chain-of-Thought, Tree-of-Thought, Algorithm-of-Thought, Sketch-of-Thought, Chain-of-Draft) as capacity-constrained choices across tasks and models.

Load-bearing premise

The five chosen reasoning styles and five tasks are representative enough to reveal general rules about when structure helps or hurts across model sizes.

What would settle it

Running the same five styles on a new set of tasks outside the original five or on models beyond the tested 270M-120B range and finding that higher structural complexity consistently improves accuracy regardless of task or size would falsify the limited-regimes claim.

Figures

Figures reproduced from arXiv: 2509.20868 by Costas Spanos, Javad Lavaei, Junyu Guo, Ming Jin, Shangding Gu.

Figure 1
Figure 1. Figure 1: Core Logic and Structural Framework for Reasoning Style Processing [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overall Accuracy rate for each group of models across five tasks [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Token usage comparison of different models on two datasets. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Sequential reasoning methodologies: CoT follows a linear progression while CoD employs itera [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Exploratory reasoning methodologies: ToT explores multiple paths with selective pruning, while [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Sketch-of-Thought (SoT): Router-based paradigm selection with exemplar retrieval. The method [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Boxplot for three groups of model accuracy. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Accuracy Heatmap for small models 17 [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Accuracy Heatmap for medium models 18 [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Accuracy Heatmap for large models 19 [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Token Usage on GSM8k deepseek phi qwen0.5b qwen32b qwen3b qwen72b qwen7b llama8b gemma2b gemma270m Model 0 100 200 300 400 500 Tokens Generated Token Usage by Model & Reasoning Style on commonsenseqa Style CoT ToT AoT CoD SoT [PITH_FULL_IMAGE:figures/full_fig_p029_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Token Usage on CommonsenseQA 29 [PITH_FULL_IMAGE:figures/full_fig_p029_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Token Usage on LogiQA [PITH_FULL_IMAGE:figures/full_fig_p030_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Comparison of reasoning style distributions before and after training. CoD dominates both [PITH_FULL_IMAGE:figures/full_fig_p035_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Training dynamics for Qwen-7B with LoRA fine-tuning. Left panel shows training loss con [PITH_FULL_IMAGE:figures/full_fig_p036_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Training dynamics for Qwen-7B with full parameter fine-tuning. Left panel shows training [PITH_FULL_IMAGE:figures/full_fig_p037_16.png] view at source ↗
read the original abstract

Structured reasoning can improve the inference performance of large language models (LLMs), but it also introduces computational cost and control constraints. When additional reasoning structure helps, and when it instead reduces efficiency or robustness, remains poorly understood. We propose StyleBench, where we study reasoning structure as a capacity-constrained design choice rather than a fixed inference recipe. We evaluate five representative reasoning styles: Chain-of-Thought, Tree-of-Thought, Algorithm-of-Thought, Sketch-of-Thought, and Chain-of-Draft across five reasoning tasks and 15 open-source LLMs ranging from 270M to 120B parameters. We find that greater structural complexity improves accuracy only in limited regimes defined by task demands and model capacity. Search-based styles help on open-ended combinatorial problems but fail on smaller models, while concise styles achieve large efficiency gains on structured tasks without sacrificing performance. We also identify systematic failure modes in smaller models, including premature guessing and weak adherence to reasoning-control instructions. To study adaptive reasoning control, we further compare supervised and reinforcement-based strategy selection on Qwen-7B-Instruct. Supervised fine-tuning collapses to shallow style preferences, whereas GRPO learns stronger adaptive control and improves downstream performance. Together, these results clarify when structured reasoning is useful, when it is wasteful, and why learning to choose a reasoning strategy is itself a challenging inference problem, we open source the benchmark in https://github.com/JamesJunyuGuo/Style_Bench.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces StyleBench to evaluate five reasoning styles (Chain-of-Thought, Tree-of-Thought, Algorithm-of-Thought, Sketch-of-Thought, and Chain-of-Draft) on five reasoning tasks using 15 open-source LLMs ranging from 270M to 120B parameters. It claims that greater structural complexity in reasoning improves accuracy only in limited regimes determined by task demands and model capacity: search-based styles benefit open-ended combinatorial problems but underperform on smaller models, while concise styles deliver efficiency gains on structured tasks without accuracy loss. The work also compares supervised fine-tuning and GRPO for adaptive strategy selection on Qwen-7B-Instruct, finding that GRPO yields better adaptive control. The benchmark and code are open-sourced.

Significance. If the empirical patterns hold under broader validation, the results would usefully clarify the accuracy-efficiency trade-offs of structured reasoning in LLMs and highlight the difficulty of learning to select reasoning strategies. The systematic comparison across model scales and the open-sourced benchmark are strengths that support reproducibility and further work on adaptive inference. The findings on failure modes in smaller models and the contrast between SFT and GRPO add concrete observations to the literature on reasoning control.

major comments (3)
  1. [Experimental setup and results sections] The central claim that structural complexity helps only in 'limited regimes defined by task demands and model capacity' rests on patterns observed across five tasks. The manuscript should explicitly justify why these five tasks are sufficiently diverse and representative to support generalization of the reported regimes (e.g., search-based styles succeeding on combinatorial problems and concise styles on structured tasks), or provide additional tasks or analysis showing that the patterns are not artifacts of the chosen task set.
  2. [Results and evaluation methodology] The abstract and results report performance differences and efficiency gains, yet the manuscript does not appear to include statistical significance tests, confidence intervals, or details on data exclusion rules and prompt variations. Without these, it is difficult to assess whether the observed differences (particularly the regime boundaries) are robust or could be affected by post-hoc choices.
  3. [Adaptive reasoning control experiments] For the adaptive control experiments on Qwen-7B-Instruct, the comparison between supervised fine-tuning and GRPO would benefit from more detail on the reward formulation, training hyperparameters, and how strategy selection is evaluated downstream. This is load-bearing for the claim that GRPO learns stronger adaptive control.
minor comments (2)
  1. [Methodology] Clarify the exact definitions and implementation details of the five reasoning styles (especially Sketch-of-Thought and Chain-of-Draft) to ensure readers can reproduce the prompt templates.
  2. [Figures] The figures comparing efficiency and accuracy across model sizes would benefit from consistent axis scaling and explicit labeling of which styles correspond to which lines or bars.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major point below and commit to revisions that strengthen the empirical grounding and transparency of the work without altering its core claims.

read point-by-point responses

  1. Referee: [Experimental setup and results sections] The central claim that structural complexity helps only in 'limited regimes defined by task demands and model capacity' rests on patterns observed across five tasks. The manuscript should explicitly justify why these five tasks are sufficiently diverse and representative to support generalization of the reported regimes (e.g., search-based styles succeeding on combinatorial problems and concise styles on structured tasks), or provide additional tasks or analysis showing that the patterns are not artifacts of the chosen task set.

    Authors: We agree that explicit justification of task diversity is necessary to support generalization of the reported regimes. The five tasks were deliberately chosen to span open-ended combinatorial search (e.g., problems requiring exploration of multiple solution paths), structured deductive reasoning, and hybrid tasks that mix both demands, thereby covering the key axes of task openness and structure that interact with reasoning style. In the revised manuscript we will add a dedicated subsection in Experimental Setup that (i) tabulates each task by its primary demand characteristics, (ii) explains the selection criteria with reference to prior benchmarks, and (iii) reports a post-hoc consistency analysis showing that the regime boundaries (search styles on combinatorial vs. concise styles on structured) hold when tasks are grouped by these characteristics. No new tasks are required because the existing set already isolates the relevant dimensions; the added analysis will make this isolation explicit. revision: yes

  2. Referee: [Results and evaluation methodology] The abstract and results report performance differences and efficiency gains, yet the manuscript does not appear to include statistical significance tests, confidence intervals, or details on data exclusion rules and prompt variations. Without these, it is difficult to assess whether the observed differences (particularly the regime boundaries) are robust or could be affected by post-hoc choices.

    Authors: We acknowledge that the current version lacks formal statistical reporting. In the revised manuscript we will add (i) 95% confidence intervals computed over multiple prompt variations and random seeds for all reported accuracies, (ii) paired statistical tests (Wilcoxon signed-rank or t-tests with Bonferroni correction) for all claimed differences, and (iii) an explicit description of data exclusion rules (none were applied beyond standard filtering for malformed outputs) and the prompt templates used. These additions will be placed in the Results section and a new Evaluation Methodology subsection so that the robustness of the regime boundaries can be directly assessed. revision: yes

  3. Referee: [Adaptive reasoning control experiments] For the adaptive control experiments on Qwen-7B-Instruct, the comparison between supervised fine-tuning and GRPO would benefit from more detail on the reward formulation, training hyperparameters, and how strategy selection is evaluated downstream. This is load-bearing for the claim that GRPO learns stronger adaptive control.

    Authors: We agree that additional implementation detail is warranted. In the revised version we will expand the Adaptive Reasoning Control section (and add an appendix) with: (i) the exact reward function used for GRPO, including the weighting of accuracy, efficiency, and adherence terms; (ii) the full hyperparameter table (learning rate, batch size, number of epochs, KL coefficient, etc.); and (iii) the downstream evaluation protocol, which measures both final task accuracy and the frequency with which the learned policy selects the style that was optimal for each instance in the held-out set. These details will allow readers to reproduce and evaluate the claim that GRPO achieves stronger adaptive control than SFT. revision: yes

Circularity Check

0 steps flagged

Empirical benchmark with no derivational circularity

full rationale

The paper conducts an empirical evaluation of five reasoning styles on five tasks across 15 LLMs, reporting observed performance patterns and efficiency trade-offs from direct experiments. No mathematical derivations, first-principles predictions, or fitted parameters are present that could reduce to inputs by construction. Findings rely on open-sourced benchmark code and external model evaluations rather than self-referential definitions or load-bearing self-citations, rendering the study self-contained against verifiable benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Central claims rest on the representativeness of the five styles and tasks plus the assumption that observed accuracy and efficiency differences reflect genuine effects of reasoning structure rather than prompt artifacts or model-specific quirks.

axioms (1)
  • domain assumption LLMs can reliably follow and adhere to explicit reasoning-control instructions across different model sizes
    Abstract notes systematic failure modes in smaller models including weak adherence, implying this is a background assumption for interpreting style differences.

pith-pipeline@v0.9.0 · 5802 in / 1247 out tokens · 44449 ms · 2026-05-18T14:36:03.866546+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. How to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Data

    cs.CL 2026-03 conditional novelty 7.0

    TESSY creates stylistically consistent synthetic data via teacher-student token interleaving, yielding 11.25% and 6.68% gains on code benchmarks where pure teacher data causes 3.25% and 10.02% drops.

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages · cited by 1 Pith paper

  1. [1]

    Formula for the sum of an arithmetic series: The sum of the firstnnatural numbers is given by the formula: S= n(n+ 1) 2 (1) whereSis the sum of the series

    work page

  2. [2]

    • The sum of the page numbers, without the duplicate, would be n(n+1) 2

    Setting up the equation: • Let ‘n’ be the total number of pages in the book. • The sum of the page numbers, without the duplicate, would be n(n+1) 2 . • Since one page number was added twice, the incorrect sum is: n(n+1) 2 +x where ‘x’ is the page number that was added twice. We know this incorrect sum is 1986: n(n+ 1) 2 +x= 1986(2)

    work page 1986

  3. [3]

    Notice that 1986 is close to a perfect square

    Finding a pattern: We need to find a value for ‘n’ and ‘x’ that satisfies this equation. Notice that 1986 is close to a perfect square. Let’s try some values of ‘n’ and see if we can find a solution

    work page 1986

  4. [4]

    • This meansx= 1986−1830 = 156

    Trial and Error: • Ifn= 60, then n(n+1) 2 = 60(61) 2 = 1830. • This meansx= 1986−1830 = 156

    work page 1986

  5. [5]

    Therefore, the page number that was added twice is 156

    Verification: Let’s check if our answer works: 23 • Is 156 a page number in the book? Yes, it could be. Therefore, the page number that was added twice is 156. 156 This is a response produced by Llama3-8B model on Game24 task. LLM Response: Game of 24 Puzzle:Use the numbers 3, 3, 12, 12 exactly once each with operations+,−,×,÷and parentheses to make 24. G...

    work page

  6. [6]

    • Let’s try3×12 = 36, which is also too big

    Look for obvious pairs: • Notice that12×12 = 144, which is too big. • Let’s try3×12 = 36, which is also too big

    work page

  7. [7]

    Consider division: • Since we need to get to 24, let’s see if we can divide one of the larger numbers by the smaller ones:12÷3 = 4

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  8. [8]

    We can use the 4 and 3 to get 12:4×3 = 12

    Build from there: • Now we have 4, 3, 3, and 12. We can use the 4 and 3 to get 12:4×3 = 12

    work page

  9. [9]

    Combine the results: • We have12 + 12 = 24. Therefore, the solution is: 12 + 12 D.2 INCORRECTFORMATRESPONSEEXAMPLE LLM Response Problem:Letmbe the smallest integer whose cube root is of the formn+r, wherenis a positive integer andris a positive real number less than 1

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  10. [10]

    Ground Truth:n= 19 LLM Response (Chain of Thought): Let’s start by writing the equation: m1/3 =n+r wherenis a positive integer andris a positive real number less than 1 1000

    Findn. Ground Truth:n= 19 LLM Response (Chain of Thought): Let’s start by writing the equation: m1/3 =n+r wherenis a positive integer andris a positive real number less than 1 1000. 24 Sinceris less than 1 1000, we can write: 0< r < 1 1000 We can also write: n+r=m 1/3 Subtractingnfrom both sides gives: r=m 1/3 −n Sinceris a positive real number less than ...

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  11. [11]

    Multiplication and Addition: •13×2 = 26 •26−3 = 23(not 24) •26−13 = 13(not 24)

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  12. [12]

    No solution Structured of Thoughts (SoT) Response: a= 2,b= 3,c= 13,d= 13 Step 1: Consider possible combinations of operations

    Multiplication and Subtraction: •13×3 = 39 •39−13 = 26 •26−2 = 24(this looks promising) Phase 2 - Verification: Let’s verify the expression(13×3−13)−2: 13×3 = 39 39−13 = 26 26−2 = 24✓ (13×3−13)−2 Chain of Draft (CoD) Response: [Shows 8 draft attempts including(13+13−2×3) = 20,(13−3)×(2+13) = 150,(13+13)÷2+3 = 16, etc.] 26 After trying multiple combination...

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  13. [13]

    Reading newspaper one of many ways to practice your what?

    The question asks, “Reading newspaper one of many ways to practice your what?” 2. We need to determine what reading a newspaper helps to practice. Now, let’s analyze each option: A) literacy: Literacy refers to the ability to read and write. Reading a newspaper is indeed a way to practice and improve one’s literacy skills. B) knowing how to read: This is ...

    work page 2048

  14. [14]

    The style must lead to the correct answer

    work page

  15. [15]

    }, { "role

    Among all styles that produce correct answers, choose the one with the most concise response." }, { "role": "user", "content": "[Problem statement with multiple choice options]" }, { "role": "assistant", "content": "[Selected Style: CoT/CoD/ToT/SoT/AoT]" } ] } G.3 STYLESELECTIONCRITERIA The training data is constructed using a two-stage optimization proce...

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