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

1. 1Discovering variable-sized patches using reinforcement learning
2. 2End-to-end training of models for long-horizon sequence data
3. 3Applying zero-shot transfer to new, unseen data
4. 4Evaluating the performance of models using metrics such as accuracy and efficiency

ML researchers and engineers working on natural language processing and time series analysis can benefit from this approach to improve model performance and efficiency

💡 Reinforcement patching can be used to learn data-adaptive representations for long-horizon sequence data

🚀 Dynamic tokenization via reinforcement patching for long-horizon sequence data! 🤖