The article discusses the diminishing returns of increasing post-training data volume as models improve, suggesting a shift toward higher-quality datasets. It highlights Parsewave, which generates post-training data from real-world engineering problems along with evaluation traces.
- Synthetic data may become redundant once a model learns a pattern, making additional variations less useful.
- The author advocates for small amounts of challenging, realistic tasks with clear ground truth and proper review.
- Calibrating task difficulty is critical to avoid noise or lack of benefit.
- Parsewave builds its dataset from real-world engineering problems rather than relying solely on scalability.
The discussion questions what makes a sample worthy of inclusion in SFT/RL/post-training datasets and whether targeting specific failure modes is more important than scalability.