Agentic Workflows: A New Frontier for LLM Training with Synthetic Data

TL;DR;

The article discusses the potential of using agentic workflows with large language models (LLMs) to generate high-quality synthetic data for pretraining LLMs. It addresses the challenge of obtaining more training data and suggests that LLMs can learn from their own agentic processes, similar to how humans learn from reflection and practice. The high cost of token generation is identified as a barrier, but the author believes that the benefits of improved performance justify the investment.