We find that the response length of reasoning LLMs, whether trained by
reinforcement learning or supervised learning, drastically increases for
ill-posed questions with missing premises (MiP), ending up with redundant and
ineffective thinking. This newly introduced scenario exacerbates the general
overthinking issue to a large extent, which we name as the MiP-Overthinking.
Such failures are against the “test-time scaling law” but have been widely
observed on multiple datasets we curated with MiP, indicating the harm of cheap
overthinking and a lack of critical thinking. Surprisingly, LLMs not
specifically trained for reasoning exhibit much better performance on the MiP
scenario, producing much shorter responses that quickly identify ill-posed
queries. This implies a critical flaw of the current training recipe for
reasoning LLMs, which does not encourage efficient thinking adequately, leading
to the abuse of thinking patterns. To further investigate the reasons behind
such failures, we conduct fine-grained analyses of the reasoning length,
overthinking patterns, and location of critical thinking on different types of
LLMs. Moreover, our extended ablation study reveals that the overthinking is
contagious through the distillation of reasoning models’ responses. These
results improve the understanding of overthinking and shed novel insights into
mitigating the problem.