Dynamic Tokenization via Reinforcement Patching: End-to-end Training and Zero-shot Transfer
Source: arXiv:2603.26097v1
Announcement Type: Cross
Abstract
Efficiently aggregating spatial or temporal horizons to acquire compact representations has become a unifying principle in modern deep learning models. However, learning data-adaptive representations for long-horizon sequence data, particularly continuous sequences like time series, remains an open challenge. While fixed-size patching has improved scalability and performance, discovering variable-sized, data-driven patches in an end-to-end manner often necessitates reliance on soft discretization, specific backbones, or heuristic rules.
Introduction
In the rapidly evolving field of deep learning, the ability to handle long-horizon sequence data effectively is crucial. Traditional methods have struggled to balance the need for adaptive representation learning with the constraints of model architecture and computational efficiency. The introduction of Reinforcement Patching (ReinPatch) marks a significant advancement in this domain.
What is Reinforcement Patching?
ReinPatch is the first framework designed to optimize both a sequence patching policy and its corresponding downstream sequence backbone model using reinforcement learning. This innovative approach addresses key limitations of existing methods by:
- Formulating patch boundary placement as a discrete decision-making process.
- Utilizing Group Relative Policy Gradient (GRPG) for optimization, eliminating the need for continuous relaxations.
- Enabling dynamic patching policy optimization in a more intuitive manner.
Key Features
The primary advantages of the ReinPatch framework include:
- Desired Compression Rate: ReinPatch allows for strict enforcement of a specific compression rate, which frees the downstream backbone to scale efficiently.
- Hierarchical Modeling: The method naturally supports multi-level hierarchical modeling, enhancing its versatility across different applications.
- Standalone Patching Module: The design enables the patching module to be utilized independently, offering valuable insights into segmentation behaviors driven purely by performance.
Evaluation and Results
ReinPatch has been rigorously evaluated on various time-series forecasting datasets. The results indicate that it demonstrates compelling performance when compared to state-of-the-art data-driven patching strategies. The effectiveness of ReinPatch lies in its ability to adaptively learn from the data, optimizing the patching process without compromising on model performance.
Conclusion
In conclusion, Reinforcement Patching represents a notable leap forward in the realm of sequence modeling and tokenization. By combining reinforcement learning with a focus on data-driven patching strategies, this framework not only enhances the efficiency of long-horizon sequence processing but also opens the door for future advancements in the field.
As the community continues to explore the implications of ReinPatch, it is expected to provide deeper insights into the segmentation behaviors that are optimal for performance-driven neural networks, paving the way for more adaptive and efficient deep learning models.