6 papers
Adaptive Ability Decomposing for Unlocking Large Reasoning Model Effective Reinforcement Learning
Zhipeng Chen, Xiaobo Qin, Wayne Xin Zhao +2
Reinforcement learning with verifiable rewards (RLVR) has shown great potential to enhance the reasoning ability of large language models (LLMs). However, due to the limited amount…
Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models
Jialong Wu, Xiaoying Zhang, Hongyi Yuan +7
Humans construct internal world models and reason by manipulating the concepts within these models. Recent advances in AI, particularly chain-of-thought (CoT) reasoning, approximat…
Revisiting the Necessity of Lengthy Chain-of-Thought in Vision-centric Reasoning Generalization
Yifan Du, Kun Zhou, Yingqian Min +3
We study how different Chain-of-Thought (CoT) designs affect the acquisition of the generalizable visual reasoning ability in vision-language models (VLMs). While CoT data, especia…
Generative Universal Verifier as Multimodal Meta-Reasoner
Xinchen Zhang, Xiaoying Zhang, Youbin Wu +5
We introduce Generative Universal Verifier, a novel concept and plugin designed for next-generation multimodal reasoning in vision-language models and unified multimodal models, pr…
Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models
Zhipeng Chen, Xiaobo Qin, Youbin Wu +4
Reinforcement learning with verifiable rewards (RLVR), which typically adopts Pass@1 as the reward, has faced the issues in balancing exploration and exploitation, causing policies…
Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu +194
We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…