2 citations · 2 across the 12 of their papers we have counts for
4 papers · 1 filter
Extending RLVR to Open-Ended Tasks via Verifiable Multiple-Choice Reformulation
Mengyu Zhang, Siyu Ding, Weichong Yin +2
Reinforcement Learning with Verifiable Rewards(RLVR) has demonstrated great potential in enhancing the reasoning capabilities of large language models (LLMs). However, its success…
RoboOmni: Proactive Robot Manipulation in Omni-modal Context
Siyin Wang, Jinlan Fu, Feihong Liu +11
Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision-Language-Action (VLA) models for robotic manipulation. Although effective in many s…
Fusion to Enhance: Fusion Visual Encoder to Enhance Multimodal Language Model
Yifei She, Huangxuan Wu
Multimodal Large Language Models (MLLMs) have made significant progress in bridging visual perception with high-level textual reasoning. However, they face a fundamental contradict…
LLMs are Single-threaded Reasoners: Demystifying the Working Mechanism of Soft Thinking
Junhong Wu, Jinliang Lu, Zixuan Ren +4
Human cognition naturally engages with abstract and fluid concepts, whereas existing reasoning models often rely on generating discrete tokens, potentially constraining their expre…