19 citations · 45 across the 21 of their papers we have counts for
5 papers · 1 filter
Skywork-R1V3 Technical Report
Wei Shen, Jiangbo Pei, Yi Peng +8
We introduce Skywork-R1V3, an advanced, open-source vision-language model (VLM) that pioneers a new approach to visual reasoning. Its key innovation lies in effectively transferrin…
Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning
Xiaokun Wang, Peiyu Wang, Jiangbo Pei +9
We propose Skywork-VL Reward, a multimodal reward model that provides reward signals for both multimodal understanding and reasoning tasks. Our technical approach comprises two key…
Skywork Open Reasoner 1 Technical Report
Jujie He, Jiacai Liu, Chris Yuhao Liu +14
The success of DeepSeek-R1 underscores the significant role of reinforcement learning (RL) in enhancing the reasoning capabilities of large language models (LLMs). In this work, we…
A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future
Jialun Zhong, Wei Shen, Yanzeng Li +7
Reward Model (RM) has demonstrated impressive potential for enhancing Large Language Models (LLM), as RM can serve as a proxy for human preferences, providing signals to guide LLMs…
Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning
Peiyu Wang, Yichen Wei, Yi Peng +10
We present Skywork R1V2, a next-generation multimodal reasoning model and a major leap forward from its predecessor, Skywork R1V. At its core, R1V2 introduces a hybrid reinforcemen…