Virgo: A Preliminary Exploration on Reproducing o1-like MLLM
Recently, slow-thinking reasoning systems, built upon large language models
(LLMs), have garnered widespread attention by scaling the thinking time during
inference. There is also growing interest in adapting this capability to
multimodal large language models (MLLMs). Given that MLLMs handle more complex
data semantics across different modalities, it is intuitively more challenging
to implement multimodal slow-thinking systems.
  To address this issue, in this paper, we explore a straightforward approach
by fine-tuning a capable MLLM with a small amount of textual long-form thought
data, resulting in a multimodal slow-thinking system, Virgo (Visual reasoning
with long thought). We find that these long-form reasoning processes, expressed
in natural language, can be effectively transferred to MLLMs. Moreover, it
seems that such textual reasoning data can be even more effective than visual
reasoning data in eliciting the slow-thinking capacities of MLLMs. While this
work is preliminary, it demonstrates that slow-thinking capacities are
fundamentally associated with the language model component, which can be
transferred across modalities or domains. This finding can be leveraged to
guide the development of more powerful slow-thinking reasoning systems. We
release our resources at https://github.com/RUCAIBox/Virgo.
677b56b8b91035bc42259da5
Yifan Du
admin_assigned
2025-01-06T08:32:27.164Z
61d78857a21a9b49c7e8e4a9
/avatars/c7e7f84cad775be2d13fab8530bf21f5.svg
Yifan Du
user
Richard1999
677b56b8b91035bc42259da6
Zikang Liu
admin_assigned
2025-01-06T08:32:32.935Z
6448dcf1b6ac93fe6512e342
/avatars/a6441f89eabd156181bafc47c0b2f8c8.svg
Zikang Liu
user
JohnCage
677b56b8b91035bc42259da7
Yifan Li
admin_assigned
2025-01-06T08:32:39.715Z
65407faf81a8731a1c134c39
/avatars/eddc718cf16d316223bcbbd2d13cf15d.svg
Yifan Li
user
yifanli
677b56b8b91035bc42259da8
Wayne Xin Zhao
677b56b8b91035bc42259da9
Yuqi Huo
677b56b8b91035bc42259daa
Bingning Wang
677b56b8b91035bc42259dab
Weipeng Chen
admin_assigned
2025-01-06T08:33:31.102Z
6501587887b370a56ad2608e
/avatars/6779baaa8ed9032de55a2f78e1f52e20.svg
Wei-Peng Chen
user
whenfra
677b56b8b91035bc42259dac
Zheng Liu
677b56b8b91035bc42259dad
Zhongyuan Wang
677b56b8b91035bc42259dae
Ji-Rong Wen
admin_assigned
2025-01-06T08:34:39.799Z
64b8c89052b7353d8c6a1013
/avatars/cd59fffe81f6b07b4519540b8ff3d95f.svg
Ji-Rong Wen
user
jrwen
Virgo: A Preliminary Exploration on Reproducing o1-like MLLM
Recently, slow-thinking reasoning systems, built upon large language models
(LLMs), have garnered widespread attention by scaling the thinking time during
inference. There is also growing interest in adapting this capability to
multimodal large language models (MLLMs). Given that MLLMs handle more complex
data semantics across different modalities, it is intuitively more challenging
to implement multimodal slow-thinking systems.
  To address this issue, in this paper, we explore a straightforward approach
by fine-tuning a capable MLLM with a small amount of textual long-form thought
data, resulting in a multimodal slow-thinking system, Virgo (Visual reasoning
with long thought). We find that these long-form reasoning processes, expressed
in natural language, can be effectively transferred to MLLMs. Moreover, it
seems that such textual reasoning data can be even more effective than visual
reasoning data in eliciting the slow-thinking capacities of MLLMs. While this
work is preliminary, it demonstrates that slow-thinking capacities are
fundamentally associated with the language model component, which can be
transferred across modalities or domains. This finding can be leveraged to
guide the development of more powerful slow-thinking reasoning systems. We
release our resources at https://github.com/RUCAIBox/Virgo.
Fine-tuning multimodal large language models with long-form textual reasoning data enhances their slow-thinking capabilities, demonstrating the transferability of these capacities across modalities.
large language models multimodal large language models fine-tuning long-form thought slow-thinking reasoning visual reasoning textual reasoning natural language multimodal slow-thinking system