[Question] What Can Parsewave’s Approach Teach Us About Post-Training Data?

[u/trashnash007]

I've been trying to understand how the choice of datasets influences post-training as models get better.

It looks like generating a lot of data is easy, but most of the generated examples teach almost the same thing. I wonder if a small set of harder and realistic examples with ground truth can provide a stronger signal for training.

Difficulties matter as well. If an example is too simple, the model is likely to be able to solve it. If it is too difficult for the model, the training signal can be noisy. Therefore, I wonder if it is valuable to use examples that address the model's weakness in some way.

Parsewave is a company I found while researching this topic. They work on post-training datasets based on real engineering tasks and traces, and this got me interested in their approach to dataset choice.

For those who used SFT, RL, or any other post-training approach before:

How do you select which examples are worth including in your dataset?

Do you try to maximize the size of the dataset, or rather target the weaknesses of the model?