Synthetic Data for any Differentiable Target

Tristan Thrush, Sung Min Park, Herman Brunborg, Luke Bailey, Marcel Roed et al.|April 9, 2026arXiv

Key Takeaway

You can precisely control what a language model learns by automatically generating synthetic training data optimized for your exact objectives, without modifying the model architecture or training process itself.

Summary

Researchers developed Dataset Policy Gradient (DPG), a technique that uses reinforcement learning to automatically generate synthetic training data optimized for any measurable goal.

training data reasoning

Key Terms

data-attribution policy-gradient synthetic-data supervised-fine-tuning