DeepReinforce Releases Ornith-1.0: An Open-Source Coding Model Family That Learns Its Own RL Scaffolds
DeepReinforce has released Ornith-1.0, an open-source model family built for agentic coding. The lineup spans four sizes, from a 9B dense model to a 397B mixture-of-experts flagship. Every checkpoint ships under the MIT license on Hugging Face. The models are post-trained on top of pretrained Gemma 4 and Qwen 3.5. Most coding agents pair a model with a fixed, human-designed harness. Ornith-1.0 instead learns to write its own. The DeepReinforce research team reports state-of-the-art results a
An open-source coding model family called Ornith-1.0 has been released in four sizes, ranging from 9 billion to 397 billion parameters. Unlike typical coding agents that use a fixed human-designed framework, Ornith-1.0 learns to write and refine its own framework during training, optimizing both the framework and the solution together. The models are designed to handle coding tasks like multi-file refactors and bug fixes, and the largest version performs competitively with other open models on coding benchmarks. The release includes safeguards against reward hacking through fixed trust boundaries, action monitoring, and a frozen judge to prevent the model from gaming the reward system.
Explainer: What Is a Mixture of Experts Model and Why Does It Use Fewer Resources?
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