PairUni: Pairwise Training for Unified Multimodal Language Models
Unified vision-language models (UVLMs) must perform both understanding and
generation within a single architecture, but these tasks rely on heterogeneous
data and supervision, making it difficult to balance them during reinforcement
learning (RL). We propose PairUni, a unified framework that reorganizes data
into understanding-generation (UG) pairs and aligns optimization accordingly.
We first use GPT-o3 to augment single-task data, generating captions for
understanding samples and question-answer (QA) pairs for generation samples,
forming aligned pairs from the same instance. Additionally, for each generation
sample, we retrieve a semantically related understanding example to form a
retrieved pair, linking different but related data points. These paired
structures expose cross-task semantic correspondences and support consistent
policy learning. To leverage this structure, we present Pair-GPRO, a pair-aware
variant based on Group Relative Policy Optimization. It assigns a similarity
score to each pair to modulate the advantage, strengthening learning from
well-aligned examples and reducing task interference. We curate a high-quality
dataset of 16K UG pairs named PairUG for RL fine-tuning and evaluate PairUni on
the powerful Janus-Pro UVLMs. Our approach achieves balanced improvements on
various UVLMs, outperforming strong UVLM RL baselines. Code:
https://github.com/Haochen-Wang409/PairUni{github.com/Haochen-Wang409/PairUni}
6902ee8072739622ee92a8b1
Jiani Zheng
claimed_verified
2026-01-08T08:33:51.926Z
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zjn
garlicisnotmyfavor
user
garlicisnotmyfavor
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Zhiyang Teng
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Xiangtai Li
claimed_verified
2025-12-15T08:14:45.527Z
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Xiangtai Li
LXT
user
LXT
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Anran Wang
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Yu Tian
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Kunpeng Qiu
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Ye Tian
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Haochen Wang
claimed_verified
2025-11-11T19:51:44.434Z
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HaochenWang
HaochenWang
user
HaochenWang
6902ee8072739622ee92a8b9
Zhuochen Wang
PairUni: Pairwise Training for Unified Multimodal Language Models
Unified vision-language models (UVLMs) must perform both understanding and
generation within a single architecture, but these tasks rely on heterogeneous
data and supervision, making it difficult to balance them during reinforcement
learning (RL). We propose PairUni, a unified framework that reorganizes data
into understanding-generation (UG) pairs and aligns optimization accordingly.
We first use GPT-o3 to augment single-task data, generating captions for
understanding samples and question-answer (QA) pairs for generation samples,
forming aligned pairs from the same instance. Additionally, for each generation
sample, we retrieve a semantically related understanding example to form a
retrieved pair, linking different but related data points. These paired
structures expose cross-task semantic correspondences and support consistent
policy learning. To leverage this structure, we present Pair-GPRO, a pair-aware
variant based on Group Relative Policy Optimization. It assigns a similarity
score to each pair to modulate the advantage, strengthening learning from
well-aligned examples and reducing task interference. We curate a high-quality
dataset of 16K UG pairs named PairUG for RL fine-tuning and evaluate PairUni on
the powerful Janus-Pro UVLMs. Our approach achieves balanced improvements on
various UVLMs, outperforming strong UVLM RL baselines. Code:
https://github.com/Haochen-Wang409/PairUni{github.com/Haochen-Wang409/PairUni}
PairUni enhances unified vision-language models by organizing data into understanding-generation pairs and using Pair-GPRO to optimize policy learning, achieving balanced performance improvements.
Unified vision-language models reinforcement learning GPT-o3 understanding-generation pairs question-answer pairs semantic correspondences Group Relative Policy Optimization Pair-GPRO PairUG dataset Janus-Pro UVLMs