The Illusion of Thinking: Understanding the Strengths and Limitations of
  Reasoning Models via the Lens of Problem Complexity
Recent generations of language models have introduced Large Reasoning Models
(LRMs) that generate detailed thinking processes before providing answers.
While these models demonstrate improved performance on reasoning benchmarks,
their fundamental capabilities, scaling properties, and limitations remain
insufficiently understood. Current evaluations primarily focus on established
math and coding benchmarks, emphasizing final answer accuracy. However, this
evaluation paradigm often suffers from contamination and does not provide
insights into the reasoning traces. In this work, we systematically investigate
these gaps with the help of controllable puzzle environments that allow precise
manipulation of complexity while maintaining consistent logical structures.
This setup enables the analysis of not only final answers but also the internal
reasoning traces, offering insights into how LRMs think. Through extensive
experiments, we show that LRMs face a complete accuracy collapse beyond certain
complexities. Moreover, they exhibit a counterintuitive scaling limit: their
reasoning effort increases with problem complexity up to a point, then declines
despite having remaining token budget. By comparing LRMs with their standard
LLM counterparts under same inference compute, we identify three performance
regimes: (1) low-complexity tasks where standard models outperform LRMs, (2)
medium-complexity tasks where LRMs demonstrates advantage, and (3)
high-complexity tasks where both models face complete collapse. We found that
LRMs have limitations in exact computation: they fail to use explicit
algorithms and reason inconsistently across scales. We also investigate the
reasoning traces in more depth, studying the patterns of explored solutions and
analyzing the models' computational behavior, shedding light on their
strengths, limitations, and raising questions about their reasoning
capabilities.
684797863ec10bdd8ab4de72
Parshin Shojaee
claimed_verified
2025-06-10T08:43:32.697Z
6520621836008ecc88699622
/avatars/b08c00af00f1736a4f4938443e575b0e.svg
Parshin Shojaee
user
parshinsh
684797863ec10bdd8ab4de73
Iman Mirzadeh
684797863ec10bdd8ab4de74
Keivan Alizadeh
684797863ec10bdd8ab4de75
Maxwell Horton
684797863ec10bdd8ab4de76
Samy Bengio
684797863ec10bdd8ab4de77
Mehrdad Farajtabar
The Illusion of Thinking: Understanding the Strengths and Limitations of
  Reasoning Models via the Lens of Problem Complexity
Recent generations of language models have introduced Large Reasoning Models
(LRMs) that generate detailed thinking processes before providing answers.
While these models demonstrate improved performance on reasoning benchmarks,
their fundamental capabilities, scaling properties, and limitations remain
insufficiently understood. Current evaluations primarily focus on established
math and coding benchmarks, emphasizing final answer accuracy. However, this
evaluation paradigm often suffers from contamination and does not provide
insights into the reasoning traces. In this work, we systematically investigate
these gaps with the help of controllable puzzle environments that allow precise
manipulation of complexity while maintaining consistent logical structures.
This setup enables the analysis of not only final answers but also the internal
reasoning traces, offering insights into how LRMs think. Through extensive
experiments, we show that LRMs face a complete accuracy collapse beyond certain
complexities. Moreover, they exhibit a counterintuitive scaling limit: their
reasoning effort increases with problem complexity up to a point, then declines
despite having remaining token budget. By comparing LRMs with their standard
LLM counterparts under same inference compute, we identify three performance
regimes: (1) low-complexity tasks where standard models outperform LRMs, (2)
medium-complexity tasks where LRMs demonstrates advantage, and (3)
high-complexity tasks where both models face complete collapse. We found that
LRMs have limitations in exact computation: they fail to use explicit
algorithms and reason inconsistently across scales. We also investigate the
reasoning traces in more depth, studying the patterns of explored solutions and
analyzing the models' computational behavior, shedding light on their
strengths, limitations, and raising questions about their reasoning
capabilities.
Large Reasoning Models (LRMs) exhibit varying performance across task complexities, with limitations in exact computation and inconsistent reasoning, as assessed using controllable puzzle environments.
Large Reasoning Models LRMs controllable puzzle environments reasoning traces standard LLMs performance regimes exact computation reasoning capabilities