Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in
  Inference-time Scaling?
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.
685c8635696820ba1f28f24b
Mingyuan Wu
claimed_verified
2025-06-26T09:32:22.703Z
65d3b7ec8f6b98b34ee6bbe3
/avatars/53c2d4e4746147fc2559435d252e8951.svg
Mingyuan Wu
user
Mingyuan1997
685c8635696820ba1f28f24c
Meitang Li
685c8635696820ba1f28f24d
Jingcheng Yang
685c8635696820ba1f28f24e
Jize Jiang
685c8635696820ba1f28f24f
Kaizhuo Yan
685c8635696820ba1f28f250
Zhaoheng Li
685c8635696820ba1f28f251
Minjia Zhang
685c8635696820ba1f28f252
Klara Nahrstedt
Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in
  Inference-time Scaling?
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.
Inference-time techniques like decoding-time scaling and self-refinement enhance reasoning in vision-language models, with generation-based methods providing greater improvement than verification-based methods, despite RL-trained models not showing self-correction benefits.
large language models inference-time computation decoding-time scaling self-refinement self-correction self-verification reinforcement learning vision-language models majority voting best-of-N selection aha moment