Ornith-1.0 addresses a core frustration for teams building AI‑driven coding agents: the need to constantly hand‑craft scaffolding logic that wraps the model with memory, error handling, and orchestration. This manual work slows iteration, introduces bugs, and limits the agent’s ability to adapt to new codebases or task types. By learning its own scaffold during reinforcement learning, Ornith-1.0 eliminates the fixed‑harness bottleneck. The model jointly optimizes the policy and the scaffolding, so higher‑reward orchestration strategies emerge automatically without engineers writing new wrappers for every project.
Reward hacking is a common worry when the model can shape its environment. Ornith-1.0 mitigates this with three practical layers: an immutable trust boundary that keeps the model from accessing test files or verification scripts, a deterministic monitor that zero‑out any trajectory attempting banned actions, and a frozen LLM judge that acts as a veto on top of the verifier. These safeguards let teams experiment with self‑scaffolding loops while keeping the training signal trustworthy.
Deployment is straightforward. All variants are released under the MIT license on Hugging Face and expose an OpenAI‑compatible endpoint. A single command with vLLM, SGLang, or Transformers serves the model; the 9B variant fits on one 80 GB GPU, making local experimentation cheap and fast. The reasoning parser returns a separate trace, enabling debugging and better alignment with agent loops that expect structured output.
Benchmarks show the flagship 397B MoE model outperforms comparable open models on Terminal‑Bench and SWE‑Bench Verified, while the 9B and 35B versions give strong accuracy for edge or cost‑sensitive setups. Teams can start small, validate locally, then scale up to the larger checkpoint when maximum accuracy is required.
In short, Ornith-1.0 gives developers a ready‑to‑use, self‑improving foundation for coding agents, removes the scaffolding engineering overhead, builds in defenses against reward gaming, and offers a simple path from laptop to production.
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