Semi-Instruct can be further optimized in several ways to enhance model performance:
Improved Data Filtering: Implement more sophisticated filtering techniques to remove irrelevant or noisy data from the dataset. This will ensure that only high-quality and relevant data is used for training, leading to better model performance.
Enhanced Test Case Generation: Refine the process of generating test cases by incorporating more diverse scenarios and edge cases. This will help the model understand a wider range of problem-solving strategies and improve its ability to generalize.
Fine-tuning Hyperparameters: Experiment with different hyperparameters such as learning rate, batch size, and optimizer settings to find the optimal configuration for training on Semi-Instruct data.
Regular Model Evaluation: Continuously evaluate the model's performance on validation datasets and fine-tune it based on feedback received during evaluation.