More Than the Final Answer: Improving Visual Extraction and Logical Consistency in Vision-Language Models
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914200644747264 |
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| author | Just, Hoang Anh Fan, Yifei Zhao, Handong Gu, Jiuxiang Zhang, Ruiyi Jenni, Simon Kafle, Kushal Jia, Ruoxi Shi, Jing |
| author_facet | Just, Hoang Anh Fan, Yifei Zhao, Handong Gu, Jiuxiang Zhang, Ruiyi Jenni, Simon Kafle, Kushal Jia, Ruoxi Shi, Jing |
| contents | Reinforcement learning from verifiable rewards (RLVR) has recently been extended from text-only LLMs to vision-language models (VLMs) to elicit long-chain multimodal reasoning. However, RLVR-trained VLMs still exhibit two persistent failure modes: inaccurate visual extraction (missing or hallucinating details) and logically inconsistent chains-of-thought, largely because verifiable signals supervise only the final answer. We propose PeRL-VL (Perception and Reasoning Learning for Vision-Language Models), a decoupled framework that separately improves visual perception and textual reasoning on top of RLVR. For perception, PeRL-VL introduces a VLM-based description reward that scores the model's self-generated image descriptions for faithfulness and sufficiency. For reasoning, PeRL-VL adds a text-only Reasoning SFT stage on logic-rich chain-of-thought data, enhancing coherence and logical consistency independently of vision. Across diverse multimodal benchmarks, PeRL-VL improves average Pass@1 accuracy from 63.3% (base Qwen2.5-VL-7B) to 68.8%, outperforming standard RLVR, text-only reasoning SFT, and naive multimodal distillation from GPT-4o. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_12487 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | More Than the Final Answer: Improving Visual Extraction and Logical Consistency in Vision-Language Models Just, Hoang Anh Fan, Yifei Zhao, Handong Gu, Jiuxiang Zhang, Ruiyi Jenni, Simon Kafle, Kushal Jia, Ruoxi Shi, Jing Computer Vision and Pattern Recognition Reinforcement learning from verifiable rewards (RLVR) has recently been extended from text-only LLMs to vision-language models (VLMs) to elicit long-chain multimodal reasoning. However, RLVR-trained VLMs still exhibit two persistent failure modes: inaccurate visual extraction (missing or hallucinating details) and logically inconsistent chains-of-thought, largely because verifiable signals supervise only the final answer. We propose PeRL-VL (Perception and Reasoning Learning for Vision-Language Models), a decoupled framework that separately improves visual perception and textual reasoning on top of RLVR. For perception, PeRL-VL introduces a VLM-based description reward that scores the model's self-generated image descriptions for faithfulness and sufficiency. For reasoning, PeRL-VL adds a text-only Reasoning SFT stage on logic-rich chain-of-thought data, enhancing coherence and logical consistency independently of vision. Across diverse multimodal benchmarks, PeRL-VL improves average Pass@1 accuracy from 63.3% (base Qwen2.5-VL-7B) to 68.8%, outperforming standard RLVR, text-only reasoning SFT, and naive multimodal distillation from GPT-4o. |
| title | More Than the Final Answer: Improving Visual Extraction and Logical Consistency in Vision-Language Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.12487 |