Role-RL: Online Long-Context Processing with Role Reinforcement Learning for Distinct LLMs in Their Optimal Roles
<|reference_start|>Role-RL: Online Long-Context Processing with Role Reinforcement Learning for Distinct LLMs in Their Optimal Roles: Large language models (LLMs) with long-context processing are still challenging because of their implementation complexity, training efficiency and data sparsity. To address this issue, a new paradigm named Online Long-context Processing (OLP) is proposed when we process a document of unlimited length, which typically occurs in the information reception and organization of diverse streaming media such as automated news reporting, live e-commerce, and viral short videos. Moreover, a dilemma was often encountered when we tried to select the most suitable LLM from a large number of LLMs amidst explosive growth aiming for outstanding performance, affordable prices, and short response delays. In view of this, we also develop Role Reinforcement Learning (Role-RL) to automatically deploy different LLMs in their respective roles within the OLP pipeline according to their actual performance. Extensive experiments are conducted on our OLP-MINI dataset and it is found that OLP with Role-RL framework achieves OLP benchmark with an average recall rate of 93.2% and the LLM cost saved by 79.4%. The code and dataset are publicly available at: https://anonymous.4open.science/r/Role-RL.<|reference_end|>
@article{he2024role-rl:,
    title={Role-RL: Online Long-Context Processing with Role Reinforcement Learning
  for Distinct LLMs in Their Optimal Roles},
    author={Lewei He, Tianyu Shi, Pengran Huang, Bingzhi Chen, Qianglong Chen,
  Jiahui Pan},
    journal={arXiv preprint arXiv:2409.18014},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18014},
    primaryClass={cs.AI}
}