AI 精选动态
智能评分 62
Ornith-1.0 开源编码专用 LLM 发布
AI 推荐理由
差异点：该模型创新性地通过 RL 同时优化代码生成 scaffold 和解决方案，在多个 agentic coding 基准上刷新开源 SOTA。核心解读
Ornith 发布 Ornith-1.0 系列开源 LLM，参数规模包括 9B Dense、31B Dense、35B MoE 和 397B MoE，在多个编码基准上达到开源 SOTA，例如 SWE-Bench Verified 82.4、Terminal-Bench 2.1 77.5。模型基于 gemma4 和 qwen3.5 后训练，采用联合优化 scaffold 和解决方案的强化学习策略，全部 MIT 许可证发布。
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AK (@_akhaliq) 转发了 Ornith (@ornith_) 的帖子：
🐦Chirp chirp! Ornith-1.0-35B is now available in 🤗 HuggingFace Claude!
🤗Come and push Ornith on the swing !
🔗https://t.co/GStiFdDueD
> **引用原帖 Ornith (@ornith_):**
> Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding.
> Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks including:
> ✅Terminal-Bench 2.1(77.5)
> ✅SWE-Bench(82.4 on verified, 62.2 on pro, 78.9 on Multilingual)
> ✅NL2Repo(48.2)
> ✅SWE Atlas(41.2 on QnA, 42.6 RF, 39.1 TW)
> ✅ClawEval(77.1)
> Post-trained on top of gemma4 and qwen3.5, Ornith-1.0 employs a novel self-improving training strategy in which reinforcement learning is used to generate not only solution rollouts, but also the task-specific scaffolds that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model generate higher-quality solutions in agentic coding.😎
> All models are released under the MIT license, enabling full commercial and research use.
> 📖Tech Blog: https://t.co/qT9N2HYWFn
> 🤗Huggingface: https://t.co/PRrwqjeBtM
> https://x.com/ornith_/status/2070148887067963854