VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied
  Iterative Policy Optimization
Authors: name: Yunxin Li; name: Xinyu Chen; name: Zitao Li; name: Zhenyu Liu; name: Longyue Wang; name: Wenhan Luo; name: Baotian Hu; name: Min Zhang
Published: 2025-05-25 06:41:28

Abstract: Applying Reinforcement Learning (RL) to Video Large Language Models
(Video-LLMs) shows significant promise for complex video reasoning. However,
popular Reinforcement Fine-Tuning (RFT) methods, such as outcome-based Group
Relative Policy Optimization (GRPO), are limited by data preparation
bottlenecks (e.g., noise or high cost) and exhibit unstable improvements in the
quality of long chain-of-thoughts (CoTs) and downstream performance.To address
these limitations, we propose VerIPO, a Verifier-guided Iterative Policy
Optimization method designed to gradually improve video LLMs' capacity for
generating deep, long-term reasoning chains. The core component is
Rollout-Aware Verifier, positioned between the GRPO and Direct Preference
Optimization (DPO) training phases to form the GRPO-Verifier-DPO training loop.
This verifier leverages small LLMs as a judge to assess the reasoning logic of
rollouts, enabling the construction of high-quality contrastive data, including
reflective and contextually consistent CoTs. These curated preference samples
drive the efficient DPO stage (7x faster than GRPO), leading to marked
improvements in reasoning chain quality, especially in terms of length and
contextual consistency. This training loop benefits from GRPO's expansive
search and DPO's targeted optimization. Experimental results demonstrate: 1)
Significantly faster and more effective optimization compared to standard GRPO
variants, yielding superior performance; 2) Our trained models exceed the
direct inference of large-scale instruction-tuned Video-LLMs, producing long
and contextually consistent CoTs on diverse video reasoning tasks; and 3) Our
model with one iteration outperforms powerful LMMs (e.g., Kimi-VL) and long
reasoning models (e.g., Video-R1), highlighting its effectiveness and
stability.