Ornith-1.5 uses self-generated tasks for RL, with three model sizes beating comparable open-source models on coding and agent benchmarks

Ornith-1.5: From Self-Scaffolding to Self-Improvement

Ornith-1.5 extends the self-scaffolding idea from Ornith-1.0 into a full self-improvement loop: the model proposes tasks, builds scaffolds, generates solution rollouts, and improves via RL. The 397B MoE flagship scores 86.1 on Terminal-Bench 2.1 and 56.0 on DeepSWE, matching Claude Opus 4.8 (85.0, 59.0) and beating GLM-5.2 and DeepSeek-V4-Flash-0731. The 35B MoE activates only 3B parameters per token yet outperforms Gemma 4-31B and Meta Muse Glimmer-30B on agentic coding. The 9B dense model has a quantized mobile version that runs on phones and scores 47.0 on Terminal-Bench 2.1 and 70.6 on SWE-Bench Verified, beating many larger models. Task reward multiplies validity, frontier difficulty (targeting a 20% success rate), and novelty. The post does not disclose training compute, data scale, or a release timeline.

Why it matters: Ornith-1.5 turns self-improvement into a full loop, and the 397B variant essentially matches Claude Opus 4.8 on Terminal-Bench and DeepSWE — a real open-source catch-up moment. Score isn't higher because Ornith isn't a tier-1 lab yet; community reproduction and real-world depl...