synthetic-self-improve-rl

Iteratively post-train a model by generating synthetic datasets that target its weaknesses and training on them in a loop until a specified budget is exhausted. You can invoke the process with your dataset, model ID, and budget parameters.

Improve model accuracy by automatically generating synthetic datasets that mimic real-world scenarios and continuously training the model to target its specific weaknesses. This iterative learning loop enhances performance across tasks like image recognition and NLP.

Yes, iterative synthetic data training can be applied to natural language processing, image recognition, and decision-making systems. The process requires access to a machine learning framework and the ability to generate synthetic data.

Set a continuous model improvement budget by specifying the desired hours in the invocation command, such as `--budget=10h`. You can also define maximum iterations and batch size to control the post-training loop.

The main limitation is that training on generated synthetic datasets requires a machine learning framework and the ability to generate synthetic data. The iterative loop also runs until a defined time or iteration budget is exhausted, bounding the improvement scope.