One limitation of the current approach is the computational complexity associated with training on long token sequences, especially for more complex planning problems. To improve scalability to even more complex planning problems, several strategies can be employed:
Curriculum Learning: Start with simpler tasks and gradually increase the complexity of the training data to allow the model to learn progressively more challenging planning strategies.
Hierarchical Planning: Integrate hierarchical planning methods and temporal abstractions into the model to enable it to abstract over multiple time steps and states, reducing the search space for complex planning tasks.
Efficient Heuristics: Incorporate better heuristics or value functions into the planning algorithm, similar to Monte Carlo Tree Search (MCTS), to limit the depth of exploration and improve efficiency in finding optimal solutions.
Model Adaptation: Fine-tune the model on a diverse set of planning tasks to improve its generalization and adaptability to different problem domains.
By implementing these strategies, the current approach can be enhanced to handle more complex planning problems efficiently and effectively.