RECURSIVE SYNTHETIC IMPROVEMENT

Summary from the TLDR AI newsletter

Frontier model progress increasingly depends on recursive synthetic data loops across judges, corpora, teachers, curricula, and RL environments. Models now generate, evaluate, and improve training data themselves, while scalable environments and on-policy learning become critical for extending capabilities beyond imitation.