WildReward: Learning Reward Models from In-the-Wild Human Interactions
Reward models (RMs) are crucial for the training of large language models (LLMs), yet they typically rely on large-scale human-annotated preference pairs. With the widespread deployment of LLMs, in-the-wild interactions have emerged as a rich source of implicit reward signals. This raises the question: Can we develop reward models directly from in-the-wild interactions? In this work, we explore this possibility by adopting WildChat as an interaction source and proposing a pipeline to extract reliable human feedback, yielding 186k high-quality instances for training WildReward via ordinal regression directly on user feedback without preference pairs. Extensive experiments demonstrate that WildReward achieves comparable or even superior performance compared to conventional reward models, with improved calibration and cross-sample consistency. We also observe that WildReward benefits directly from user diversity, where more users yield stronger reward models. Finally, we apply WildReward to online DPO training and observe significant improvements across various tasks. Code and data are released at https://github.com/THU-KEG/WildReward.
698ab50a1b2dc6b37d61afdb
Hao Peng
698ab50a1b2dc6b37d61afdc
Yunjia Qi
698ab50a1b2dc6b37d61afdd
Xiaozhi Wang
698ab50a1b2dc6b37d61afde
Zijun Yao
698ab50a1b2dc6b37d61afdf
Lei Hou
698ab50a1b2dc6b37d61afe0
Juanzi Li
WildReward: Learning Reward Models from In-the-Wild Human Interactions
Reward models (RMs) are crucial for the training of large language models (LLMs), yet they typically rely on large-scale human-annotated preference pairs. With the widespread deployment of LLMs, in-the-wild interactions have emerged as a rich source of implicit reward signals. This raises the question: Can we develop reward models directly from in-the-wild interactions? In this work, we explore this possibility by adopting WildChat as an interaction source and proposing a pipeline to extract reliable human feedback, yielding 186k high-quality instances for training WildReward via ordinal regression directly on user feedback without preference pairs. Extensive experiments demonstrate that WildReward achieves comparable or even superior performance compared to conventional reward models, with improved calibration and cross-sample consistency. We also observe that WildReward benefits directly from user diversity, where more users yield stronger reward models. Finally, we apply WildReward to online DPO training and observe significant improvements across various tasks. Code and data are released at https://github.com/THU-KEG/WildReward.
WildReward demonstrates that reward models can be effectively trained from in-the-wild user interactions using ordinal regression, achieving performance comparable to traditional methods while benefiting from user diversity.
reward models large language models ordinal regression in-the-wild interactions user feedback online DPO training