Inverse Scaling in Test-Time Compute
Authors: name: Aryo Pradipta Gema, name: Alexander Hägele, name: Runjin Chen, name: Andy Arditi, name: Jacob Goldman-Wetzler, name: Kit Fraser-Taliente, name: Henry Sleight, name: Linda Petrini, name: Julian Michael, name: Beatrice Alex, name: Pasquale Minervini, name: Yanda Chen, name: Joe Benton, name: Ethan Perez

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.