You now know how the agent reasons through each experiment cycle: read, hypothesize, edit, train, measure, keep or revert.
You've seen the specific experiments agents try (batch size, depth, attention patterns, learning rates) and how overnight runs progress from high-impact changes to diminishing returns. Next, you'll learn how to write program.md to steer these runs and get the most out of each overnight session.