synthetic-self-improve-rl

What problem does it solve?

This Skill automates the process of iteratively improving a machine learning model by generating synthetic datasets that target the model's weaknesses, and then post-training the model on these datasets in a loop until a budget is exhausted.

Core Features & Use Cases

• Model Improvement: Post-train a smaller model on synthetic datasets targeting its weaknesses.
• Synthetic Data Generation: Automatically generate datasets that mirror the real-world environment.
• Iterative Training: Continuously train and evaluate the model to improve its performance.
• Use Case: Use this Skill to improve a machine learning model's accuracy on a specific task by generating and training on synthetic data that mimics real-world scenarios.

Quick Start

To use the synthetic-self-improve-rl skill, invoke it with the desired dataset, model, and other parameters: /synthetic-self-improve-rl <dataset> [--model=<hf-id>] [--budget=10h] [--hub-id=<owner/env>] [--max-iters=15] [--batch-size=512] [--init-from=<path>]