Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in Inference-time Scaling?

Understand

Inference time techniques such as decoding time scaling and self refinement have been shown to substantially improve mathematical reasoning in large language models (LLMs), largely attributed to emergent self correction and self verification behaviors often elicited through reinforcement learning (RL).

• In this work, we ask whether the same recipe transfers to vision language models (VLMs), especially RL finetuned variants that claim strong visual mathematical reasoning.
• Through extensive evaluation, we reach three main findings that differ markedly from text only models.
• First, generation time capability matters more than verification and refinement: simple majority voting consistently and substantially outperforms verification centric strategies such as best of N with self verification.