Ornith-1.0 presents open models for agentic coding
Published on June 25, 2026, Ornith-1.0 is a family of open models ranging from 9 billion to 397 billion parameters. Reinforcement learning is used to optimize both code solutions and the scaffolds that guide them.
The announcement says Ornith-1.0 comprises open models ranging from 9 billion to 397 billion parameters. Training uses reinforcement learning to optimize both code solutions and the specific scaffolds that guide them.
Engineers can evaluate the models and the self-scaffolding approach in agentic coding workflows. The publication is dated June 25, 2026; consult the original announcement to verify its details and reported findings. If using AI to study or apply the material, do not submit internal code or data without authorization, and follow your organization’s data policy.