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X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains
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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.
Forward citations
Cited by 7 Pith papers
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VOLD: Reasoning Transfer from LLMs to Vision-Language Models via On-Policy Distillation
VOLD transfers text-only LLM reasoning to a 3B VLM via teacher-trace SFT followed by GRPO plus on-policy distillation, improving visual-math accuracy over GRPO-alone and prior text-only baselines (e.g., MathVision 28....
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MMReason: An Open-Ended Multi-Modal Multi-Step Reasoning Benchmark for MLLMs Toward AGI
MMReason is an open-ended multimodal reasoning benchmark that filters out guessable and memorized questions and scores model answers both by final answer and by intermediate steps.
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Reinforcing General Reasoning without Verifiers
VeriFree trains LLMs with RL by maximizing the likelihood of the reference answer after generated reasoning, matching verifier-based RL without any verifier.
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R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO
Share-GRPO creates paraphrased and visually augmented versions of reasoning questions and shares answers and reward signals across versions, improving multimodal reasoning without cold-start SFT.
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Infi-MMR: Curriculum-based Unlocking Multimodal Reasoning via Phased Reinforcement Learning in Multimodal Small Language Models
Infi-MMR-3B trains a 3B visual language model with text-first, caption-augmented, then caption-free reinforcement learning, reaching 43.68% on MathVerse testmini.
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Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle
A survey that maps reinforcement learning methods, datasets, benchmarks, and open-source tools across the full training lifecycle of large language models, focusing on verifiable-reward reasoning.
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Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models
A survey-style position paper claims that reinforcement fine-tuning powers reasoning in multimodal LLMs, summarizing over a hundred recent works and proposing five future research directions.
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