The secret sauce here is how the model was trained. Typically, coding models are trained on static snapshots of code from GitHub and other public sources. They basically learn what good code looks like at a single point in time. IQuest did something totally different. They trained their model using entire commit history of repositories.

This approach added a temporal component to training, allowing the model to learn how code actually changes from one commit to the next. It saw how entire projects evolve over months and even years. It learned the patterns in how developers refactor and improve code, and the real world workflows of how software gets built. Instead of just learning what good code looks like, it learned how code evolves.

Coding is inherently an iterative process where you make an attempt at a solution, and then iterate on it. As you gain a deeper understanding of the problem, you end up building on top of existing patterns and evolving the codebase over time. IQuest model gets how that works because it was trained on that entire process.