title: X-Reasoner: Towards Generalizable Reasoning Across Modalities and
    Domains
summary: 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.
paper_authors._id: 681d7ee755699177c7fb636a
paper_authors.name: Qianchu Liu
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paper_authors.name: Guanghui Qin
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paper_authors.name: Timothy Ossowski
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paper_authors.name: Yu Gu
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paper_authors.name: Ying Jin
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paper_authors.name: Sid Kiblawi
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paper_authors.fullname: Sid Kiblawi
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paper_authors.name: Sam Preston
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paper_authors.fullname: Sam Preston
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paper_authors.user: RustyArchimedes
paper_authors._id: 681d7ee755699177c7fb6372
paper_authors.name: Mu Wei
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paper_authors.name: Paul Vozila
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paper_authors.fullname: Paul Vozila
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paper_authors.name: Tristan Naumann
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paper_authors.fullname: Tristan Naumann
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paper_authors.name: Hoifung Poon
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paper_title: X-Reasoner: Towards Generalizable Reasoning Across Modalities and
    Domains
paper_summary: 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.
paper_ai_summary: X-Reasoner, a vision-language model post-trained on general-domain text, achieves strong reasoning capabilities across modalities and domains, with X-Reasoner-Med outperforming existing models on medical benchmarks.
paper_ai_keywords: multimodal reasoning text-only reasoning models long chain-of-thoughts supervised fine-tuning reinforcement learning verifiable rewards vision-language model X-Reasoner X-Reasoner-Med