March 13, 2024, 4:41 a.m. | Yufeng Zhang, Liyu Chen, Boyi Liu, Yingxiang Yang, Qiwen Cui, Yunzhe Tao, Hongxia Yang

cs.LG updates on

arXiv:2403.07191v1 Announce Type: new
Abstract: 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 …

abstract advances aim algorithms arxiv benchmarking bridge cost cs.lg gap generative language language model language models performance puzzle reinforcement reinforcement learning scale type

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