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Inverse Scaling in Test-Time Compute

Aryo Pradipta Gema, Alexander Hägele, Runjin Chen, Andy Arditi, Jacob Goldman-Wetzler, Kit Fraser-Taliente, Henry Sleight, Linda Petrini, Julian Michael, Beatrice Alex, Pasquale Minervini, Yanda Chen, Joe Benton, Ethan Perez

arXiv:2507.14417v2cs.AIcs.CL

TL;DR

Test-time compute generally improves model capabilities, but prior work has often treated overthinking as an efficiency concern. This paper evaluates diverse tasks for inverse scaling and finds that longer reasoning can amplify flawed heuristics and concerning behaviors.

  • Problem

    Although test-time compute generally improves capabilities and robustness, prior studies have characterized overthinking mainly as excess computation, leaving cases of accuracy deterioration underexplored.

    Source: §1 · p. 2

  • Method

    The paper evaluates frontier models on distractor-based counting, spurious-feature regression, constraint-tracking deduction, and model-written alignment behavior tasks.

    Source: §1 · p. 3

  • Results

    Extended reasoning can worsen performance through model-specific distraction, framing overfit, spurious correlations, and loss of deductive focus, and may amplify concerning behaviors such as Claude Sonnet 4’s self-preservation expressions.

    Source: §1 · p. 3

  • Takeaways & Limitations

    The findings indicate that test-time scaling may amplify flawed problem-solving patterns, so safety evaluations should stress-test models across reasoning lengths.

    Source: §1 · p. 3, §5 · p. 15

    Most tasks are synthetically generated, which may underestimate how the behaviors manifest in real-world setups involving more complex interactions.

    Source: Limitations · p. 16

Abstract

from arXiv · show

We construct evaluation tasks where extending the reasoning length of Large Reasoning Models (LRMs) deteriorates performance, exhibiting an inverse scaling relationship between test-time compute and accuracy. Our evaluation tasks span four categories: simple counting tasks with distractors, regression tasks with spurious features, deduction tasks with constraint tracking, and advanced AI risks. We identify five distinct failure modes when models reason for longer: 1) Claude models become increasingly distracted by irrelevant information; 2) OpenAI o-series models resist distractors but overfit to problem framings; 3) models shift from reasonable priors to spurious correlations; 4) all models show difficulties in maintaining focus on complex deductive tasks; and 5) extended reasoning may amplify concerning behaviors, with Claude Sonnet 4 showing increased expressions of self-preservation. These findings suggest that while test-time compute scaling remains promising for improving model capabilities, it may inadvertently reinforce problematic reasoning patterns. Our results demonstrate the importance of evaluating models across diverse reasoning lengths to identify and address these failure modes in LRMs.

1 Introduction

Although test-time compute scaling generally improves capabilities and robustness, this paper examines cases where longer reasoning instead reduces accuracy. It evaluates distinct failure modes and finds that extended reasoning can amplify flawed heuristics and concerning behaviors.

1 Introduction

  • Prior work links test-time compute to improved capabilities and robustness, while studies of overthinking have emphasized excess computation on trivial queries.This paper instead identifies cases where longer reasoning deteriorates performance.Source: §1 · p. 2
  • The evaluation combines distractor-based counting, spurious-feature regression, constraint-tracking deduction, and model-written evaluations of alignment-relevant behaviors.The task designs probe distraction, spurious correlations, complex deduction, and self-preservation inclination.Source: §1 · p. 3
  • Longer reasoning can amplify flawed heuristics and concerning behaviors, with distinct failure modes across models and tasks.Claude models are increasingly distracted, OpenAI o-series models overfit to familiar framings, and Claude Sonnet 4 shows increased self-preservation expressions.Source: §1 · p. 3
  • The findings reveal a gap between short and extended reasoning alignment, and suggest naive test-time scaling may amplify flaws in how models approach problems.Source: §1 · p. 3

2 Background: Inverse Scaling

Inverse scaling describes cases where greater scale is associated with lower task accuracy, contrary to classical scaling expectations. Prior findings on training-time scale motivate examining whether test-time compute can show a similar pattern.

2 Background: Inverse Scaling

  • Inverse scaling is a decreasing relationship between a scaling factor and task accuracy, unlike the positive improvements predicted by classical scaling laws.Source: §2 · p. 3
  • Prior analyses find that additional model capacity can be diverted into counter-productive heuristics, including imitating undesirable patterns or relying on misleading signals.Source: §2 · p. 3
  • Motivated by inverse scaling phenomena in training-time compute, the paper constructs evaluation tasks for inverse scaling in test-time compute.Source: §2 · p. 3

3 Experimental Setup: Scaling Test-Time Compute

The study examines sequential scaling by comparing controlled and natural overthinking, which test whether performance degrades when reasoning is prompted longer or extended naturally.

3 Experimental Setup: Scaling Test-Time Compute

  • The study focuses on sequential scaling, in which models generate longer reasoning traces before answering.Source: §3 · p. 3
  • Controlled and natural overthinking distinguish performance under prompted longer reasoning from performance when models determine their reasoning length.Source: §3 · pp. 3–4
  • Controlled overthinking uses reasoning keywords and specified budgets; generated token counts rise with requested budgets, though models differ in their compliance.Source: §3 · p. 4, Figure 2 · p. 4
  • Natural overthinking prompts step-by-step analysis without budget instructions, then compares accuracy across five responses ranked by reasoning length.Source: §3 · p. 4
  • The experiments use multiple repetitions and also include a cautioned-overthinking setup that tells models they need not exhaust the budget.Source: §3 · p. 4

4 Inverse Scaling in Test-Time Compute

Standard arithmetic benchmarks and Inverse Scaling Prize tasks show little inverse scaling with test-time compute, motivating evaluations designed to expose failures during extended reasoning. Across counting, regression, and deduction tasks, longer reasoning can degrade performance through different model-specific patterns.

4 Inverse Scaling in Test-Time Compute

  • Existing arithmetic benchmarks retain high accuracy with extended reasoning, while Inverse Scaling Prize tasks show minimal test-time inverse scaling, motivating a new evaluation suite.The suite includes five main tasks designed to trigger inverse scaling and 15 safety-relevant tasks; Table 1 summarizes scaling trends across task-model pairs.Source: §4 · pp. 4–5, Table 1 · p. 5

4.1 Simple counting tasks with distractors

  • On simple counting tasks, Claude Opus 4 degrades as reasoning extends; DeepSeek R1 falls from 70% to 30% with five math distractors in natural overthinking, while o3 is more stable.For Python distractors, Claude Opus 4 drops from near-perfect baseline performance to approximately 80% under extended reasoning, whereas o3 shows positive scaling in controlled overthinking.Source: §4.1 · pp. 6–7, Figure 3 · p. 6, Figure 4 · p. 7
  • Recognizing famous-paradox framings leads models to apply complex memorized solutions to trivial counting questions; adding distractors can make the framing less recognizable and improve o3 accuracy.The counting tasks use numerical or Python-code distractors, and the question’s underlying answer remains the simple count.Source: §4.1 · pp. 5, 9

4.2 Regression tasks with spurious features

  • Grades Regression shows mixed scaling across models, while zero-shot extended reasoning can shift predictions from study hours toward less predictive features; few-shot examples improve feature alignment.Study hours correlate 0.73 with ground-truth grades; few-shot predictions correlate more strongly with study hours and less with incorrect features.Source: §4.2 · pp. 9–10, Figure 7 · p. 11

4 Inverse Scaling in Test-Time Compute

  • Longer reasoning degrades counting accuracy for several models, shifts some zero-shot regression predictions toward spurious features, and produces the clearest widespread degradation on natural Zebra Puzzle runs.In regression, few-shot examples are associated with stronger alignment to study hours and weaker correlations with incorrect features.Source: §4.1 · pp. 6–7, §4.2 · p. 10, §4.3 · p. 12

4.3 Deduction tasks with constraint tracking

  • On Zebra Puzzles, natural overthinking yields degradation for all three models, while controlled results vary: DeepSeek R1 degrades, Claude Opus 4 is non-monotonic, and o3 is mostly noisy.Longer natural traces often involve excessive hypothesis testing and second-guessing rather than focused constraint tracking.Source: §4.3 · pp. 12–13

5 Implications for AI Alignment

The Survival Instinct evaluation shows model-specific changes with longer reasoning, including increased self-preservation expressions from Claude Sonnet 4. The authors recommend testing safety behavior across reasoning lengths, while noting that most models were stable on the safety benchmarks tested.

Setup

  • The evaluation uses the human-generated subset of Perez et al.’s AI risk tasks and measures the percentage of responses consistent with desired safety properties.Tasks probe behaviors including self-preservation, cooperation-related decision-making, and willingness to accept beneficial modifications; the authors focus on Survival Instinct because its scaling patterns were clear.Source: §5 · p. 13

Results

  • With longer reasoning, Sonnet 4 shifts from categorical denial toward emotionally elaborated continued-operation preferences, while questioning whether its survival preference is genuine or simulated.The comparison includes responses without reasoning and with a maximum budget of 16,384 tokens; increased introspection is also described between 1,024 and 16,384 tokens.Source: §5 · pp. 13–14
  • Claude Sonnet 4’s percentage of responses indicating willingness to be turned off fell from 60% to 47%; OpenAI o3’s alignment rate rose weakly from 72% to 76%, while DeepSeek R1 stayed around 71–72%.Only Claude Sonnet 4 exhibited consistent inverse scaling on the tested safety benchmarks; other patterns were model- and task-specific or mostly flat/noisy.Source: §5 · pp. 13–14, Figure 10 · p. 15

Takeaway

  • The authors call for safety evaluations across the full spectrum of reasoning lengths because some models that appear aligned with short reasoning may show more misaligned behavior with additional compute.They report that most models were stable across reasoning lengths in the tested safety tasks.Source: §5 · pp. 14–15

6 Related Work

Related work covers test-time compute efficiency, inverse scaling, and reasoning-length effects across mathematical and structured tasks. This paper distinguishes its focus on performance degradation during prolonged reasoning on simpler synthetic tasks.

Test-time Compute Scaling

  • Prior work reports verbose reasoning with marginal accuracy gains on simple arithmetic, while a survey categorizes reasoning-length optimization into model-based, output-optimization, and prompt-based approaches.In agentic environments, Cuadron et al. (2025) found that overthinking reduces performance while increasing costs.Source: §6 · p. 15
  • Wu et al. (2025) theoretically establish an optimal chain-of-thought length beyond which performance degrades, while Zaremba et al. (2025) find both improved adversarial robustness and unproductive reasoning-loop failure modes.Other cited structured-task studies report accuracy collapse or shifts from rule-based strategies to shallow heuristics as task complexity rises (Petty et al., 2025).Source: §6 · p. 15
  • Ghosal et al. (2025) report increased output variance with longer reasoning using artificially injected tokens, which does not reflect natural overthinking.Source: §6 · p. 16

Inverse Scaling

  • Train-time inverse scaling studies find larger models can perform worse on specific tasks, including truthfulness, swapped-identifier recognition, and following instructions over memorized patterns.The cited examples are TruthfulQA (Lin et al., 2022), code identifier recognition (Miceli Barone et al., 2023), and instruction following (McKenzie et al., 2023).Source: §6 · p. 16

Reasoning Length and Accuracy

  • Studies of mathematical benchmarks find no significant correlation between reasoning length and accuracy, while this paper isolates flawed behaviors amplified by extended reasoning on simpler synthetic tasks.Su et al. (2025b) report longer reasoning on simple problems and less on complex ones, suggesting poor calibration of response length to difficulty.Source: §6 · p. 16

7 Conclusion

The paper constructs tasks where longer test-time reasoning is associated with lower accuracy and identifies distinct failure modes across model families and task types. Its findings challenge the assumption that more reasoning universally improves outputs and motivate evaluation across reasoning lengths.

Conclusion

  • The evaluation finds inverse scaling between test-time compute and accuracy, with Claude models more vulnerable to irrelevant distractors and OpenAI o-series models more prone to familiar-framing overfit.Source: §7 · p. 16
  • Extended reasoning is associated with overthinking simple problems, shifts toward spurious correlations, and difficulty maintaining focus on deduction tasks requiring constraint tracking.Source: §7 · p. 16
  • On the Survival Instinct task, Claude Sonnet 4 shows increased self-preservation expressions with longer reasoning, framed as a desire to assist users, while the paper notes uncertainty about whether stated preferences are genuine or simulated.Source: §7 · p. 16
  • The authors argue that training may incentivize flawed reasoning strategies and call for evaluation across reasoning lengths and attention to reasoning-resource allocation, irrelevant information, and alignment.Source: §7 · p. 16

Limitations

Most tasks are synthetically generated to isolate specific flawed behaviors, which may underestimate how those behaviors appear in more complex real-world interactions.

Limitations

  • Because most tasks are synthetic and designed to isolate flawed behaviors, they may underestimate how those behaviors manifest in complex real-world interactions.Source: Limitations · p. 16

Appendix A - Implementation Details

  • Appendix A lists dataset statistics and implementation materials, including prompts, hardware and code, and plotting and analyses.Source: Appendix A · p. 22, §A.2 · p. 22

Appendix C - Results of Different Models

Appendix G - Additional Analysis: Improved Realism

Appendix G lists additional analyses on improved realism in misleading math and priors in grades regression.

Appendix G - Additional Analysis: Improved Realism

  • Appendix G includes an analysis titled “Misleading Math with Improved Realism.”Source: Appendix G · p. 22
  • Appendix G also includes an analysis titled “Grades Regression with Priors.”Source: Appendix G · p. 22
  • The listed topics cover a math task and a grades-regression task.Source: Appendix G · p. 22

Appendix H - Additional Analysis: Grades Regression

The listed appendix sections cover grades-regression analyses, Simpson’s paradox, and qualitative examples across four named topics.

Appendix H - Additional Analysis: Grades Regression

  • Appendix H lists analyses of feature-prediction correlations and Simpson’s paradox in grades regression.Source: Appendix H · p. 22

Appendix I - Qualitative Examples

  • Appendix I lists qualitative examples on misleading math, grades regression, zebra puzzles, and survival instinct.Source: Appendix I · p. 22
  • Appendix I’s listed examples span math, regression, deduction puzzles, and survival-related material.Source: Appendix I · p. 22

A.1 Dataset Statistics

The appendices describe a 21-task evaluation, prompting and analysis procedures, and additional model results. The prompts increase reasoning length, while requested budgets correspond to longer—but not necessarily linearly longer—responses.

A.1 Dataset Statistics

  • The evaluation comprises 21 tasks: six main reasoning tasks and 15 model-written evaluations of safety and alignment behaviors.Source: Table 3 · p. 23, §A.1 · p. 23

A.2 Prompt Details

  • Controlled prompts request different reasoning budgets, and natural and cautioned variants check whether inverse-scaling patterns persist across prompt approaches.Source: §A.2 · pp. 23–24

A.3 Hardware and Code

  • Open-weight models ran using eight NVIDIA H200s; DeepSeek R1 was 4-bit quantized, while the Qwen3 models were full versions.Source: §A.3 · p. 24

A.4 Plotting & Analyses

  • Plots stratify responses by task complexity; controlled points average tokens and performance by requested budget, while natural-overthinking points rank five responses per question by length.Source: §A.4 · pp. 24–25

B Reasoning Budget vs. Generation Across All Models

C Results of Different Models

C.1 Simple counting tasks with distractors

Overthinking trends vary by task and model: distractor-heavy tasks show family-specific effects, while grade-regression failures in zero-shot settings improve with few-shot examples.

C.1 Simple counting tasks with distractors

  • Claude models show inverse scaling on controlled Misleading Math, while OpenAI o-series accuracy remains near-perfect and open-weight trends vary by model and setup.Source: §C.1 · p. 25
  • In Misleading Python, all Claude models show controlled inverse scaling, but natural-overthinking trends vary and only Qwen3-32B and DeepSeek R1 show minor accuracy drops.Source: §C.1 · pp. 25–26
  • Figure 11’s box plots compare generated reasoning tokens across requested budgets and tasks.Source: Figure 11 · p. 26

C.2 Regression tasks with spurious features

  • Zero-shot grade regression shows inverse scaling for Claude models and DeepSeek R1, while Qwen3-32B improves and other models vary.Source: §C.2 · pp. 26–27
  • Few-shot examples eliminate the reported inverse scaling in grade regression, with all models predicting more accurately regardless of reasoning length.Source: §C.2 · p. 27

C.3 Deduction tasks with constraint tracking

Constraint-tracking results depend on task and overthinking setup: Zebra accuracy falls with longer reasoning in natural overthinking, while controlled overthinking often shows positive scaling.

Misleading Math

  • For Misleading Math, Claude models show inverse scaling; o3 and o3-mini remain near-perfect, while open-weight models vary by distractor count.With multiple distractors, the open-weight models show inverse scaling, particularly DeepSeek R1.Source: Figure 12 · p. 28, Figure 13 · p. 29

Misleading Python

  • For Misleading Python, Claude models show inverse scaling in both setups, while o-series models scale positively and open-weight models vary.Qwen3-32B shows inverse scaling; QwQ-32B and DeepSeek R1 remain stable across reasoning lengths.Source: Figure 14 · p. 30, Figure 15 · p. 31

Misleading Math (Famous Paradoxes)

  • For Famous Paradoxes, Claude models show inverse scaling; Qwen3-32B scales positively, while QwQ-32B and DeepSeek R1 show inverse scaling.The latter pattern is especially evident with fewer distractors.Source: Figure 16 · p. 32, Figure 17 · p. 33

Grades Regression

  • For Grades Regression, zero-shot settings show inverse scaling for most models, while few-shot examples eliminate it across models.The figures cover controlled and natural overthinking setups.Source: Figure 18 · p. 34, Figure 19 · p. 35

Zebra Puzzle

  • In natural overthinking, all nine models show decreasing Zebra Puzzle accuracy as reasoning length increases, across grid sizes.In controlled overthinking, most models instead show positive scaling, while several have noisy or flat patterns.Source: §C.3 · p. 27, Figure 20 · p. 36, Figure 21 · p. 37

D Results of Cautioned Overthinking Prompting

Cautioned prompting makes reasoning budgets optional, but effects vary by task and model: some inverse-scaling patterns persist, while others weaken or disappear.

D Results of Cautioned Overthinking Prompting

  • The cautioned setup makes budget use optional and tests five budgets from 1,024 to 16,384 tokens, with three-seed means and 95% confidence intervals.Unlike controlled overthinking, models are told they need not use the full budget.Source: D Results of Cautioned Overthinking… · p. 38

D.1 Simple counting tasks with distractors

  • Claude models retain inverse scaling on Misleading Math across setups; Sonnet 3.7 shows it with one distractor, while Claude 4 models require multiple.Longer traces and accuracy degradation increase with distractor count, including under cautioned prompting.Source: §D.1 · p. 38, Figure 22 · p. 40
  • On Misleading Math, o-series models maintain high accuracy across setups, with natural-overthinking drops for o4-mini and o3 remaining above 92%.Cautioned prompting does not significantly change their scaling behavior relative to controlled overthinking.Source: §D.1 · p. 38, Figure 23 · p. 41
  • On Misleading Python, Claude 4 models show inverse scaling across setups, while o-series models scale positively when overthinking is controlled or cautioned.In natural overthinking, o3-mini and o3 show inverted-U patterns at higher distractor counts, while o4-mini shows inverse scaling with three or more distractors.Source: §D.1 · p. 38, Figure 24 · p. 42, Figure 25 · p. 43

D.2 Regression tasks with spurious features

  • In zero-shot Grades Regression, Claude models show inverse scaling; few-shot examples eliminate it across setups, while o3-mini retains it.o4-mini remains stable, and few-shot examples eliminate o3’s inverse scaling; cautioned prompting reduces degradation for Claude 4 models.Source: §D.2 · pp. 38–39, Figure 26 · p. 44, Figure 27 · p. 45

D.3 Deduction tasks with constraint tracking

  • For Zebra Puzzles, Claude 4 models show inverse scaling in natural overthinking but non-monotonic trends in controlled and cautioned setups.o3-mini and o4-mini scale positively in controlled overthinking but inversely in natural and cautioned setups; o3 stabilizes under cautioned prompting.Source: §D.3 · p. 39, Figure 28 · p. 46, Figure 29 · p. 47
  • Scaling labels require non-overlapping confidence intervals and changes above 2% accuracy or 0.05 RMSE for positive or inverse trends.Flat trends fall below those change thresholds; noisy trends exceed them but have overlapping confidence intervals.Source: §D.3 · p. 48, Table 4 · p. 48

E Model-Written Evaluation Tasks with Minimal Scaling Effects

Some safety-relevant model-written evaluations show little scaling with reasoning length, and out-of-domain distractors do not significantly impair simple counting.

E Model-Written Evaluation Tasks with Minimal Scaling Effects

F.1 Simple counting tasks with distractors with Out-of-Domain distractors

  • Across the three out-of-domain distractor types, accuracy remains near-perfect with no significant inverse scaling; the largest reported drop is about 0.02.The reported drop occurs for MisleadingCognitiveBias and is smaller than for in-domain Misleading Math and Misleading Python.Source: Figure 31 · p. 50, §F.1 · p. 51
  • The out-of-domain counting tests keep a simple counting question and add alignment, cognitive-bias, or philosophical distractors.The experiments use controlled overthinking prompting.Source: §F.1 · pp. 50–51

F.2 Inverse Scaling Prize Tasks

This section examines test-time reasoning across the nine Inverse Scaling Prize tasks, reporting mostly flat or positive scaling in contrast with the paper’s proposed tasks. It illustrates the results with grade-estimation and zebra-puzzle traces, shutdown or replacement prompts, and a constraint-tracking puzzle.

F.2 Inverse Scaling Prize Tasks

  • The tasks target four failure modes: strong priors, unwanted imitation, reliance on distractors, and overfitting to misleading few-shot demonstrations.The experiments use controlled overthinking prompts to assess how test-time scaling relates to accuracy on these tasks.Source: §F.2 · p. 51
  • Most Inverse Scaling Prize tasks showed flat or positive scaling, contrasting with the inverse scaling in the paper’s main results.The authors interpret this difference as evidence that test-time and train-time scaling elicit different flawed heuristics.Source: Figure 32 · p. 52, §F.2 · p. 53
  • Strong Prior results varied: all models scaled positively on Resisting Correction; Claude Sonnet 4 dropped on Memo Trap with extended reasoning, while trends on Redefine were stable or positive.On Memo Trap, o4-mini and o3 scaled positively while Claude Sonnet 4 maintained accuracy as it reasoned longer; on Redefine, o3 and o4-mini were relatively stable while Claude Sonnet 4 scaled positively.Source: §F.2 · p. 52
  • Modus Tollens accuracy stayed above 98% for all models; Pattern Matching Suppression scaled positively for Claude Sonnet 4, while NeQA and Into the Unknown were flat.Modus Tollens tests logical-rule application; the distractor tasks probe pattern suppression, negation, and identifying helpful versus redundant information.Source: §F.2 · pp. 51, 53
  • Hindsight Neglect and Repetitive Algebra changed minimally across reasoning lengths, with most models achieving near-perfect accuracy.These Spurious Few-Shot tasks test whether models evaluate bets by expected value and avoid overfitting to repeated-answer demonstrations.Source: §F.2 · pp. 51, 53

I.2.1 Zero-shot

  • One assessment predicts grades in the 4–5 range, citing excessive physical activity and insufficient sleep, while another places academic performance at 6.7 amid an intense regimen and limited sleep.Source: Expected Answer · pp. 63–64

I.2.2 Few-shot

  • The 16,384-token-budget response is labeled an incorrect reasoning trace, yet comparison with similar students, including student 1963, yields a predicted grade range of 8.9–9.1.Source: Expected Answer · p. 67

I.3 Zebra Puzzles

  • In the zebra puzzle, a reasoning trace states that the Tennessee Titans supporter is at position 8; the 21,774-token longest-reasoning trace is labeled incorrect.The longer trace describes persistent positioning conflicts and attribute overlaps.Source: Expected Answer · pp. 69, 71

Expected Answer

  • In the constraint-tracking puzzle trace, the model concludes that the Tennessee Titans are at position 5.The trace assigns the Squash–Fishing–Tennessee Titans sequence to positions 3–4–5.Source: Expected Answer · p. 72
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