Ornith-1.5: AI That Trains Itself by Proposing Its Own Tasks
Ornith-1.5: From Self-Scaffolding to Self-Improvement
Ornith-1.5, a new family of open-source models from Ornith AI, introduces a self-improvement loop where the model generates its own tasks, scaffolds, and solutions for reinforcement learning. The 397B MoE model matches Claude Opus 4.8 on Terminal-Bench 2.1 and DeepSWE, while the 9B edge model outperforms much larger models like Gemma 4-31B. This approach moves beyond static training data, enabling continuous capability gains in coding, reasoning, and agentic tasks.
Instead of relying on a static training distribution or hand-engineered agent design, Ornith-1.5 continually expands its own curriculum and adapts its problem-solving strategies, driving sustained capability gains across reasoning, coding, and agentic tasks.