$\mathbf(N,K)$-Puzzle: A Cost-Efficient Testbed for Benchmarking Reinforcement Learning Algorithms in Generative Language Model
<|reference_start|>$\mathbf(N,K)$-Puzzle: A Cost-Efficient Testbed for Benchmarking Reinforcement Learning Algorithms in Generative Language Model: Recent advances in reinforcement learning (RL) algorithms aim to enhance the performance of language models at scale. Yet, there is a noticeable absence of a cost-effective and standardized testbed tailored to evaluating and comparing these algorithms. To bridge this gap, we present a generalized version of the 24-Puzzle: the $(N,K)$-Puzzle, which challenges language models to reach a target value $K$ with $N$ integers. We evaluate the effectiveness of established RL algorithms such as Proximal Policy Optimization (PPO), alongside novel approaches like Identity Policy Optimization (IPO) and Direct Policy Optimization (DPO).<|reference_end|>
@article{zhang2024$\mathbf{(n,k)}$-puzzle:,
    title={$\mathbf{(N,K)}$-Puzzle: A Cost-Efficient Testbed for Benchmarking
  Reinforcement Learning Algorithms in Generative Language Model},
    author={Yufeng Zhang, Liyu Chen, Boyi Liu, Yingxiang Yang, Qiwen Cui, Yunzhe
  Tao, Hongxia Yang},
    journal={arXiv preprint arXiv:2403.07191},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07191},
    primaryClass={cs.LG cs.AI cs.CL}
}