X-Reasoner: Towards Generalizable Reasoning Across Modalities and
  Domains
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.
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Qianchu Liu
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2025-05-09T08:59:18.501Z
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qianchu liu
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qianchu
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Sheng Zhang
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2025-05-09T07:21:22.974Z
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Sheng Zhang
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shengz
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Guanghui Qin
admin_assigned
2025-05-09T08:59:24.320Z
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Guanghui Qin
user
hiaoxui
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Timothy Ossowski
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Yu Gu
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Ying Jin
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Sid Kiblawi
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2025-05-09T08:59:37.798Z
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Sid Kiblawi
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sidkiblawi
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Sam Preston
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2025-05-09T08:59:43.654Z
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Sam Preston
user
RustyArchimedes
681d7ee755699177c7fb6372
Mu Wei
681d7ee755699177c7fb6373
Paul Vozila
admin_assigned
2025-05-09T09:00:01.673Z
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Paul Vozila
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Paulvozila
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Tristan Naumann
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2025-05-09T09:00:15.476Z
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Tristan Naumann
user
tnaumann
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Hoifung Poon
admin_assigned
2025-05-09T09:00:22.468Z
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/avatars/1bfa6d8f82e9223b47630cefd79d7d0e.svg
Hoifung Poon
user
hoifung
X-Reasoner: Towards Generalizable Reasoning Across Modalities and
  Domains
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.
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.
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