huggingface : https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B-GGUF
Official documentation : https://deep-reinforce.com/ornith_1_0.html
Korean blog introduction : https://javaexpert.tistory.com/1783
Cutting-edge coding agent : Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (Gemma 4 and Qwen 3.5 fine-tuned) versions, it achieves top performance among open-source models of similar scale on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo, and OpenClaw.
Self-Improving Learning Framework : Ornith-1.0 utilizes reinforcement learning (RL) to learn not only the solution derivation process but also the scaffold that guides it. By simultaneously optimizing the scaffold and the resulting solution, the model discovers better exploration paths and generates higher quality solutions.
I don't quite understand how self-improvement is applied...
According to Reddit reviews, it's said to use a think loop.
Since it's Qwen 3.5 fine-tuning, it seems to be another Qwen derivative model.
I haven't had much success using derivative models, so I'm not expecting much.
I'll try it out for fun.
