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Ornith-1.5: From Self-Scaffolding to Self-Improvement
Today, we are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends the self-scaffolding framework
introduced in Ornith-1.0 into a more complete self-improvement loop: the model
proposes new tasks, generates task-specific scaffolds, and produces solution rollouts for reinforcement learning, continuously creating new learning
experiences from which it can improve.
Ornith-1.5 spans three model scales: 397B MoE, 35B MoE and 9B dense. Designed for strong general-purpose intelligence across reasoning, agentic, and coding tasks, Ornith-1.5
achieves state-of-the-art performance among open-source models of comparable size across a broad range of benchmarks. Ornith-1.5-397B scores 86.1 on Terminal-Bench 2.1 and 56.0
on DeepSWE, performing on par with Claude Opus 4.8 (85.0 and 59.0) while outperforming leading open-source models of similar scale, including GLM-5.2 (82.7 and 46.2) and
DeepSeek-V4-Flash-0731 (82.7 and 54.4). At the other end of the spectrum, Ornith-1.5-9B, with its quantized Ornith-1.5-9B-Mobile version, can be readily deployed on iPhone and
Android devices while substantially outperforming larger models such as Gemma 4-31B and Qwen 3.6-35B.
At the flagship scale, Ornith-1.5-397B achieves 86.1 on Terminal-Bench 2.1 and 56 on DeepSWE, matching Claude Opus 4.8 on both benchmarks and outperforming leading
open-source models of similar size, including GLM-5.2 and DeepSeek-V4-Flash-0731.
Ornith-1.5-35B significantly outperforms its similar-sized peer Qwen 3.6-35B across all coding and agentic benchmarks, and despite activating only 3B parameters per token,
it also outperforms dense models—Gemma 4-31B and Meta’s Muse Glimmer-30B—by wide margins on agentic coding (68.5 vs. 43.4 and 51.7 on Terminal-Bench 2.1; 79.0 vs. 52.0 and
76.0 on SWE-Bench Verified).
The edge-deployable Ornith-1.5-9B also delivers remarkably strong results, achieving 47.0 on Terminal-Bench 2.1 and 70.6 on SWE-Bench Verified. Despite being a compact
9B-parameter model, it matches or exceeds the performance of much larger models such as Gemma 4-31B and Qwen 3.6-35B.
Ornith-1.5 extends Ornith-1.0 by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and
solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers
effective strategies for solving them, and improves the policy through reinforcement learning.
Each training cycle proceeds in three stages. Given an environment or codebase, high-level instructions about the task type, and access to the model’s previous task-solving
history, the system proposes progressively harder tasks that go beyond what the model has already solved, exposing capability gaps and continuously pushing the training
frontier.
For each task, the model then generates or refines a task-specific scaffold—the instructions, tools, decomposition strategy, and orchestration used to approach the problem.
Conditioned on the task and scaffold, the policy produces a solution rollout. Reward from the rollout is propagated across all three stages, so the system learns not only to
produce better solutions, but also to generate more useful training tasks and construct more effective scaffolds.
Repeated over training, this creates a closed self-improvement loop in which stronger policies enable the generation of harder and more informative tasks, evolving scaffolds
discover better ways to elicit the model’s capabilities, and higher-quality rollouts provide increasingly effective learning signals. 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