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[[{“value”:”Ornith 1: Open-Weight Agentic Coding Models That Write Their Own Harnesses (and Beat Bigger Models)
In this video: I break down Ornith 1, a new family of open-weight models built for agentic coding that can outperform much larger models on benchmarks like Terminal Bench, with the 397B model nearing closed-source performance (Opus 4.8). The key idea isn’t just scores—it’s how Ornith is trained to generate both solution rollouts and a task-specific harness (memory, retries, error handling) in a single loop, using reinforcement learning (GRPO) so rewards update both the solution and the scaffold. I cover reward-hacking risks and the three-layer defenses: locked boundaries, deterministic monitoring, and a frozen judge model. I also share my own Ollama tests on an M2 Max comparing Qwen 3.5 9B base vs Ornith 1 9B (8-bit): similar accuracy, but Ornith is ~3× cheaper (up to 20× on some tasks), while long-horizon “honesty under pressure” seems to require larger scale (35B+).
LINKS:
https://deep-reinforce.com/ornith_1_0.html
https://huggingface.co/collections/deepreinforce-ai/ornith-10
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00:00 Ornith Models Overview
02:46 Self Written Harnesses
04:31 Reward Hacking Risks
05:30 Three Layer Defenses
06:17 My Ollama Test Setup
07:40 Private Bench Results
08:33 Long Horizon Honesty Test
09:53 Key Takeaways on 9B
10:42 Caveats”}]] Read More Prompt Engineering
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