@inproceedings{chen-etal-2025-drbo,
    title = "{DRBO}: Mitigating Short Board Effect via Dynamic Reward Balancing in Multi-reward {LLM} Optimization",
    author = "Chen, Nuo  and
      Gao, Yufei  and
      Jin, Yongnan  and
      Hu, Yan  and
      Gao, Anningzhe  and
      Yan, Lingyong  and
      Wang, Benyou",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.468/",
    doi = "10.18653/v1/2025.findings-emnlp.468",
    pages = "8817--8841",
    ISBN = "979-8-89176-335-7",
    abstract = "In the current landscape of large language models (LLMs), many evaluation metrics have been developed and used as rewards during training to improve specific metrics. However, balancing these metrics and dynamically adjusting reward weights remains challenging, as current approaches often fail to enhance weaker metrics. To address this, we empirically propose a Dynamic Reward Balancing Optimization framework DRBO to mitigate the ``short-board effect'' by measuring performance, adjusting reward weights to prioritize weaker metrics, and optimizing the model via reinforcement learning. We apply DRBO to both single-task and multi-type task scenarios, validating its effectiveness in generation with citations and online shopping conversation tasks. The results demonstrate improved overall performance and balanced optimization across multiple metrics, effectively overcoming the diversity and complexity inherent in LLMs. Our codes are available at https://github.com/NuoJohnChen/DRBO."
}