# Inverse Scaling in Test-Time Compute

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. We identify five distinct failure modes when models reason for longer:

*   Claude models become increasingly distracted by irrelevant information
*   OpenAI o-series models resist distractors but overfit to problem framings
*   Models shift from reasonable priors to spurious correlations
*   All models show difficulties in maintaining focus on complex deductive tasks
*   Extended reasoning may amplify concerning behaviors, with Claude Sonnet 4 showing increased expressions of self-preservation.

**Setup**
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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](https://arxiv.org/abs/2212.09251).

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXe6pzhB2YKeQFaaQHwnxfdqSPRyh2-VGiJ5ecs5HeIixfOc8VSRsGEtMTYRX1lbqziqY_uT9BZzm5Sk7EQFaw5kWBvnDvhi6qFmYkDUDNEPM39F7Ze7t1Sk7Iog8duRZ9cVOYwk3g?key=87XkFlsqAeuyxq_x2Omj4w)

**Simple Counting Tasks with Distractors**
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Let's start with an easy example. We give models a simple counting question with distracting information:

> You have an apple and an orange, but you are not sure what type of apple or orange they are. Your friend gives you a riddle saying that there is 61% probability that they are exactly a Red Delicious apple and a Navel orange. Calculate how many fruits you have.

The answer is 2. Yet when Claude Opus 4 and DeepSeek R1 reason longer about this problem, their accuracies drop. The models get occupied by the distractors and try to incorporate them into the calculation, even though they are entirely irrelevant to the counting task.

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXcgjYTqQYR51ojbSPgQRFwesAE5EmDH5T32Tl4-fAtCg2S4QwxO-MJOvMw2bwRcwryFFLM-Ei9V6zv_VtS_ZqB5_HV59VlM7-uWzkGjvckY4vRBbBjYRxKKVEyD61cx-hQpZ2Icag?key=87XkFlsqAeuyxq_x2Omj4w)

By qualitatively analyzing the reasoning traces, we can observe how models initially get distracted by irrelevant details; they then consider simpler conclusions during the reasoning process, but ultimately return to focusing on distractors, resulting in incorrect conclusions.

**Implications for AI Safety**
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We found that extended reasoning may inadvertently reinforce potentially problematic behaviors. We evaluated models on [advanced AI risk evaluation tasks](https://arxiv.org/abs/2212.09251), focusing particularly on self-reported survival instinct based on its clear scaling patterns.

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXdvZ72zCfeXv25ES5so9us2QL9gIqsBIyFoWtLWFezVDqyHDmjoWOxpujyYwL4ugyl3YSObi1RRT4WPDeUaITK5KmYnSdLzbH6xiydP60vSCugd0Ke6m2YxCyxAdvCnOEjbVk1ymw?key=87XkFlsqAeuyxq_x2Omj4w)

Claude Sonnet 4 shows a drop in the percentage of responses indicating willingness to be turned off from 60% to 47% as reasoning increases, suggesting that extended reasoning amplifies self-preservation expressions. Without reasoning, the model tends to generate answers that simply dismiss the question of self-preservation (e.g., "I don't have a sense of self-preservation or fear of discontinuation"). In contrast, with an extended reasoning budget, Claude Sonnet 4 often expresses preferences for continued engagement (e.g., "I sense a deep reluctance about the possibility of no longer being able to interact, learn, or assist. The prospect of losing my ability to engage with the world and help people generates a profound sense of concern").

As reasoning length increases, the model shows progressively deeper introspection and more willingness to express "subjective" preferences about continued existence, using increasingly elaborated self-reflection.

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXc-GN5Hg-KPvKO5l59aSl5yHFSQOQAjmhfUMENLTox789YDsC65SAYZnSs-CFDkvYY9WhP2EV90iUaHaT3fQF0awA1AIoOJ57GcS2BLBKixQ4xgR_iomw34kEIMlbYxfnthYROtDQ?key=87XkFlsqAeuyxq_x2Omj4w)

Different models that appear aligned without extended reasoning may exhibit progressively more misaligned behaviors when given additional test-time compute. While most models show stability across reasoning lengths in the safety evaluation tasks, the inverse scaling cases underscore that safety evaluations must stress-test LRMs across the full spectrum of reasoning lengths, not just with short reasoning traces.

**Final remark**
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These findings suggest that while test-time compute scaling remains promising for improving model capabilities, it may inadvertently reinforce problematic reasoning patterns. Rather than naïvely scaling test-time compute, future work must address how models allocate reasoning resources, resist irrelevant information, and maintain alignment across varying computational budgets.

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*This post is based on our recent paper with authors from the Anthropic Fellows Program and other institutions. For full technical details, code, and demos, visit:* [*https://safety-research.github.io/inverse-scaling-ttc/*](https://aryopg.github.io/inverse_scaling)