X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908354364833792 |
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| author | Liu, Qianchu Zhang, Sheng Qin, Guanghui Ossowski, Timothy Gu, Yu Jin, Ying Kiblawi, Sid Preston, Sam Wei, Mu Vozila, Paul Naumann, Tristan Poon, Hoifung |
| author_facet | Liu, Qianchu Zhang, Sheng Qin, Guanghui Ossowski, Timothy Gu, Yu Jin, Ying Kiblawi, Sid Preston, Sam Wei, Mu Vozila, Paul Naumann, Tristan Poon, Hoifung |
| contents | Recent proprietary models (e.g., o3) have begun to demonstrate strong multimodal reasoning capabilities. Yet, most existing open-source research concentrates on training text-only reasoning models, with evaluations limited to mainly mathematical and general-domain tasks. Therefore, it remains unclear how to effectively extend reasoning capabilities beyond text input and general domains. This paper explores a fundamental research question: Is reasoning generalizable across modalities and domains? Our findings support an affirmative answer: General-domain text-based post-training can enable such strong generalizable reasoning. Leveraging this finding, we introduce X-Reasoner, a vision-language model post-trained solely on general-domain text for generalizable reasoning, using a two-stage approach: an initial supervised fine-tuning phase with distilled long chain-of-thoughts, followed by reinforcement learning with verifiable rewards. Experiments show that X-Reasoner successfully transfers reasoning capabilities to both multimodal and out-of-domain settings, outperforming existing state-of-the-art models trained with in-domain and multimodal data across various general and medical benchmarks (Figure 1). Additionally, we find that X-Reasoner's performance in specialized domains can be further enhanced through continued training on domain-specific text-only data. Building upon this, we introduce X-Reasoner-Med, a medical-specialized variant that achieves new state of the art on numerous text-only and multimodal medical benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03981 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains Liu, Qianchu Zhang, Sheng Qin, Guanghui Ossowski, Timothy Gu, Yu Jin, Ying Kiblawi, Sid Preston, Sam Wei, Mu Vozila, Paul Naumann, Tristan Poon, Hoifung Artificial Intelligence Computation and Language Machine Learning Recent proprietary models (e.g., o3) have begun to demonstrate strong multimodal reasoning capabilities. Yet, most existing open-source research concentrates on training text-only reasoning models, with evaluations limited to mainly mathematical and general-domain tasks. Therefore, it remains unclear how to effectively extend reasoning capabilities beyond text input and general domains. This paper explores a fundamental research question: Is reasoning generalizable across modalities and domains? Our findings support an affirmative answer: General-domain text-based post-training can enable such strong generalizable reasoning. Leveraging this finding, we introduce X-Reasoner, a vision-language model post-trained solely on general-domain text for generalizable reasoning, using a two-stage approach: an initial supervised fine-tuning phase with distilled long chain-of-thoughts, followed by reinforcement learning with verifiable rewards. Experiments show that X-Reasoner successfully transfers reasoning capabilities to both multimodal and out-of-domain settings, outperforming existing state-of-the-art models trained with in-domain and multimodal data across various general and medical benchmarks (Figure 1). Additionally, we find that X-Reasoner's performance in specialized domains can be further enhanced through continued training on domain-specific text-only data. Building upon this, we introduce X-Reasoner-Med, a medical-specialized variant that achieves new state of the art on numerous text-only and multimodal medical benchmarks. |
| title | X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2505.03981 |