1 citations · 1 across the 8 of their papers we have counts for
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Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model
Xue Wang, Tian Zhou, Jinyang Gao +2
We present a joint forecasting framework for time series prediction that contrasts with traditional direct or recursive methods. This framework achieves state-of-the-art performanc…
Incentivizing Strong Reasoning from Weak Supervision
Yige Yuan, Teng Xiao, Shuchang Tao +4
Large language models (LLMs) have demonstrated impressive performance on reasoning-intensive tasks, but enhancing their reasoning abilities typically relies on either reinforcement…
RePO: Understanding Preference Learning Through ReLU-Based Optimization
Junkang Wu, Kexin Huang, Xue Wang +5
Aligning large language models (LLMs) with human preferences is critical for real-world deployment, yet existing methods like RLHF face computational and stability challenges. Whil…
Larger or Smaller Reward Margins to Select Preferences for Alignment?
Kexin Huang, Junkang Wu, Ziqian Chen +6
Preference learning is critical for aligning large language models (LLMs) with human values, with the quality of preference datasets playing a crucial role in this process. While e…