X-Reasoner: Towards Generalizable Reasoning Across Modalities and
  Domains
Authors: name: Qianchu Liu, name: Sheng Zhang, name: Guanghui Qin, name: Timothy Ossowski, name: Yu Gu, name: Ying Jin, name: Sid Kiblawi, name: Sam Preston, name: Mu Wei, name: Paul Vozila, name: Tristan Naumann, name: Hoifung Poon

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.