Skywork-Reward-V2: Leading the New Milestone for Open-Source Reward Models
25 Articles
25 Articles
Skywork-Reward-V2: Leading the New Milestone for Open-Source Reward Models
SINGAPORE, July 5, 2025 /PRNewswire/ -- In September 2024, Skywork first open-sourced the Skywork-Reward series models and related datasets. Over the past nine months, these models and data have been widely adopted by the open-source community for research and practice,…
Skywork-Reward-V2: Leading the new milestone for open-source reward models
In September 2024, Skywork first open-sourced the Skywork-Reward series models and related datasets. Over the past nine months, these models and data have been widely adopted by the open-source community for research and practice, with over 750,000 cumulative downloads on the HuggingFace platform, helping multiple frontier models achieve excellent results in authoritative evaluations such as RewardBench. On July 4, 2025, Skywork continues to ope…
Skywork-Reward-V2: Leading the New Milestone for Open-Source Reward Models - AI-Tech Park
In September 2024, Skywork first open-sourced the Skywork-Reward series models and related datasets. Over the past nine months, these models and data have been widely adopted by the open-source community for research and practice, with over 750,000 cumulative downloads on the HuggingFace platform, helping multiple frontier models achieve excellent results in authoritative evaluations such as RewardBench. On July 4, 2025, Skywork continues to ope…
SynPref-40M and Skywork-Reward-V2: Scalable Human-AI Alignment for State-of-the-Art Reward Models
Understanding Limitations of Current Reward Models Although reward models play a crucial role in Reinforcement Learning from Human Feedback (RLHF), many of today’s top-performing open models still struggle to reflect the full range of complex human preferences. Even with sophisticated training techniques, meaningful progress has been limited. A major reason appears to be the shortcomings in current preference datasets, which are often too narrow…
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