Runs a global hill-climb optimization loop where the parent is always the current best candidate and the val significance gate decides acceptance. Use as the algorithm for most runs — the first run on a new project, binary pass/fail scorers, and small task sets. Pick how each iteration's reflection is focused with --focus all (every failing val task), cyclic (one task at a time), or hardest-first (lowest-scoring first). Switch to gepa when rollouts are expensive and per-task feedback is rich, or skillopt when you want an annealed edit budget.
