VIDEOP2R: Video Understanding from Perception to Reasoning
Reinforcement fine-tuning (RFT), a two-stage framework consisting of supervised fine-tuning (SFT) and reinforcement learning (RL) has shown promising results on improving reasoning ability of large language models (LLMs). Yet extending RFT to large video language models (LVLMs) remains challenging. We propose VideoP2R, a novel process-aware video RFT framework that enhances video reasoning by modeling perception and reasoning as distinct processes. In the SFT stage, we develop a three-step pipeline to generate VideoP2R-CoT-162K, a high-quality, process-aware chain-of-thought (CoT) dataset for perception and reasoning. In the RL stage, we introduce a novel process-aware group relative policy optimization (PA-GRPO) algorithm that supplies separate rewards for perception and reasoning. Extensive experiments show that VideoP2R achieves state-of-the-art (SotA) performance on six out of seven video reasoning and understanding benchmarks. Ablation studies further confirm the effectiveness of our process-aware modeling and PA-GRPO and demonstrate that model's perception output is information-sufficient for downstream reasoning.
691e3c0d3c64d32b0364585d
Yifan Jiang
claimed_verified
2025-11-20T08:49:15.394Z
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/avatars/61014a3fd45f74ce541bdbe53929e233.svg
Yifan Jiang
YifanJ
user
YifanJ
691e3c0d3c64d32b0364585e
Yueying Wang
691e3c0d3c64d32b0364585f
Rui Zhao
claimed_verified
2026-03-23T11:03:18.273Z
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Rui Zhao
zhaoruizju
user
zhaoruizju
691e3c0d3c64d32b03645860
Toufiq Parag
691e3c0d3c64d32b03645861
Zhimin Chen
691e3c0d3c64d32b03645862
Zhenyu Liao
691e3c0d3c64d32b03645863
Jayakrishnan Unnikrishnan
VIDEOP2R: Video Understanding from Perception to Reasoning
Reinforcement fine-tuning (RFT), a two-stage framework consisting of supervised fine-tuning (SFT) and reinforcement learning (RL) has shown promising results on improving reasoning ability of large language models (LLMs). Yet extending RFT to large video language models (LVLMs) remains challenging. We propose VideoP2R, a novel process-aware video RFT framework that enhances video reasoning by modeling perception and reasoning as distinct processes. In the SFT stage, we develop a three-step pipeline to generate VideoP2R-CoT-162K, a high-quality, process-aware chain-of-thought (CoT) dataset for perception and reasoning. In the RL stage, we introduce a novel process-aware group relative policy optimization (PA-GRPO) algorithm that supplies separate rewards for perception and reasoning. Extensive experiments show that VideoP2R achieves state-of-the-art (SotA) performance on six out of seven video reasoning and understanding benchmarks. Ablation studies further confirm the effectiveness of our process-aware modeling and PA-GRPO and demonstrate that model's perception output is information-sufficient for downstream reasoning.
VideoP2R, a process-aware reinforcement fine-tuning framework, improves video reasoning and understanding by modeling perception and reasoning separately, achieving state-of-the-art results on multiple benchmarks.
Reinforcement fine-tuning supervised fine-tuning reinforcement learning large language models large video language models VideoP2R perception reasoning VideoP2R-CoT-162K chain-of-thought process-aware group relative policy optimization PA-GRPO