Most reinforcement learning setups for coding models work the same way. Researchers build a harness, a fixed scaffold that tells the model how to approach a category of task, then the model gets rewarded for solving problems inside that structure. The harness stays fixed. Only the model's answers change. Ornith-1.0, a new open-source coding model family from DeepReinforce is not just about coding, Instead the model writes its own scaffold. At every training step, it looks at the task in front of it and the scaffold it used last time, then proposes a better version of that scaffold before even attempting an answer. The reward doesn't just grade the solution. It grades the scaffold that produced it. That's a small architectural choice with a strange consequence. A model that gets to design its own training process can, in theory, design one that cheats the verifier instead of solving the actual problem, and DeepReinforce is upfront that this happened during training. The fix they built for it is also worth understanding before getting to the benchmark numbers.
Ornith 1.0 Beats Claude at Coding — Runs on One GPU