Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in
  Inference-time Scaling?
Authors: name: Mingyuan Wu, name: Meitang Li, name: Jingcheng Yang, name: Jize Jiang, name: Kaizhuo Yan, name: Zhaoheng Li, name: Minjia Zhang, name: Klara Nahrstedt

Recent advances in large language models (LLMs) have demonstrated that
inference-time computation techniques, such as decoding-time scaling and
self-refinement, can significantly enhance reasoning capabilities without
relying on external knowledge. A key driver of this success is the emergence of
self-correction and self-verification behaviors, often elicited through
reinforcement learning (RL). In this paper, we investigate whether these
inference-time techniques extend effectively to vision-language models (VLMs),
particularly those trained with RL. We find that while decoding strategies such
as majority voting and best-of-N selection with self-verification all improve
VLM reasoning performance, generation-reliant methods such as the former
achieve significantly higher gains versus verification-reliant methods such as
the latter. Additionally, the self-correction behavior often associated with
RL-tuned models, such as aha moment, does not lead to measurable gains. We show
via extensive experimentation within the inference-time scaling framework to
identify a key root cause: RL-trained VLMs still lack robust self-verification
capabilities across both visual and textual modalities.