- Implement `train_all_future.py` for training with query-conditioned all-future supervision.
- Implement `train_next_step.py` for training with next-token/next-time-point supervision.
- Introduce `train_util.py` for shared utility functions including logging, dataset splitting, and model checkpointing.
- Enhance argument parsing for both training scripts to accommodate new parameters.
- Update loss functions and model configurations to support the new training paradigms.
- Implemented target construction in `targets.py` for next-token and unique-time set supervision.
- Added validation functions and utility methods for target building.
- Created a comprehensive training script in `train.py` that includes data loading, model building, optimizer setup, and training loop with early stopping and logging.
- Integrated loss functions and readout mechanisms based on target modes.
- Established dataset splitting and DataLoader configurations for training, validation, and testing.
