Paradigm: post-training gains hinge on combining procedural and LLM-based synthetic data
tensorqt · x · 2026-10-07
Paradigm shares that post-training improvements for its math model depend heavily on combining complementary synthetic data generation mechanisms: procedural generation for controllable, verifiable problems, and LLM-based problem formulation for diversity and novelty.
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