A 4B model was trained using supervised fine-tuning and agentic reinforcement learning to produce Postgres query plans that beat Postgres's default plans, achieving a 44.7% latency reduction across 113 join-heavy queries. The model learned the harness language of the qo-agent harness through off-policy distillation and reinforcement learning, resulting in a geometric mean speedup of 1.81x and ...