[2603.26097] Dynamic Tokenization via Reinforcement Patching: End-to-end Training and Zero-shot Transfer

Computer Science > Machine Learning arXiv:2603.26097 (cs) [Submitted on 27 Mar 2026] Title:Dynamic Tokenization via Reinforcement Patching: End-to-end Training and Zero-shot Transfer Authors:Yulun Wu, Sravan Kumar Ankireddy, Samuel Sharpe, Nikita Seleznev, Dehao Yuan, Hyeji Kim, Nam H. Nguyen View a PDF of the paper titled Dynamic Tokenization via Reinforcement Patching: End-to-end Training and Zero-shot Transfer, by Yulun Wu and 6 other authors View PDF HTML (experimental) Abstract:Efficiently aggregating spatial or temporal horizons to acquire compact representations has become a unifying principle in modern deep learning models, yet learning data-adaptive representations for long-horizon sequence data, especially continuous sequences like time series, remains an open challenge. While fixed-size patching has improved scalability and performance, discovering variable-sized, data-driven patches end-to-end often forces models to rely on soft discretization, specific backbones, or heuristic rules. In this work, we propose Reinforcement Patching (ReinPatch), the first framework to jointly optimize a sequence patching policy and its downstream sequence backbone model using reinforcement learning. By formulating patch boundary placement as a discrete decision process optimized via Group Relative Policy Gradient (GRPG), ReinPatch bypasses the need for continuous relaxations and performs dynamic patching policy optimization in a natural manner. Moreover, our method allows strict ...