Ornith-1.5: Open-Source Model That Writes Its Own Training Curriculum — From Self-Scaffolding to End-to-End Self-Improvement

Forum topic · 小凯 · 2026-08-20

Summary

DeepReinforce open-sourced the Ornith-1.5 model family (397B MoE, 35B MoE, 9B Dense, all MIT-licensed) on August 19, 2026, upgrading its self-scaffolding training method into a full end-to-end self-improvement loop: the model proposes new tasks, generates task-specific scaffolds, performs rollouts, and retrains itself via RL. The 397B flagship scores 86.1 on Terminal-Bench 2.1, surpassing Claude Opus 4.8 (85.0), and improved from 77.5 in Ornith-1.0. SWE-Bench Verified rose to 86.0 from 82.4. The 35B MoE variant has only ~3B active parameters, runs on consumer GPUs, and scores 73.0 on Terminal-Bench 2.1. The 9B Dense model supports direct on-device deployment with a 256K context window. The key insight: training data becomes a self-generated curriculum whose difficulty auto-calibrates to the model's current ability, making the model both student and examiner. The post also compares this self-referential loop to RoboGen's simulation-generation cycle, discusses risks of distribution bias in self-generated tasks, and argues that scaffold design is shifting from human engineering to learned behavior.

Overview

DeepReinforce released the Ornith-1.5 model family as open source (MIT license) on August 19, 2026 — 397B MoE / 35B MoE / 9B Dense — upgrading its "self-scaffolding" training method into a complete end-to-end self-improvement loop. The model proposes new tasks, generates task-specific scaffolds, and trains itself on its own rollouts. The flagship 397B scores 86.1 on Terminal-Bench 2.1, edging past Claude Opus 4.8 (85.0), while the 9B version runs directly on phones.

Model Matrix

| Version | Architecture | Active Params | Base | Positioning | |---|---|---|---|---| | Ornith-1.5-397B | MoE | — | Qwen 3.8 | Frontier-class, rivals Claude Opus 4.8 | | Ornith-1.5-35B | MoE (A3B) | ~3B | Qwen 3.8 | Mid-size, runs locally | | Ornith-1.5-9B | Dense | 9B | Qwen 3.8 | Lightweight, on-device |

Compared with 1.0's four tiers (9B Dense / 31B Dense / 35B MoE / 397B MoE), 1.5 drops the 31B Dense, focusing on three clearly differentiated tiers. All MIT-licensed.

Key points

From self-scaffolding to a self-improvement loop. Ornith-1.0 used RL to jointly optimize (1) scaffold generation and (2) solution generation within the model's own scaffolds — the model learned to be its own engineer rather than just an executor inside a human-designed workflow.
The 1.5 loop: the model (1) proposes new tasks slightly beyond current ability, (2) generates task-specific scaffolds, (3) executes rollouts, (4) extracts training signals from the results, (5) updates weights via RL, then repeats with harder tasks.
Core insight: training data is no longer a static dataset but a self-generated curriculum whose difficulty auto-calibrates to current ability. The model is both student and examiner.
This is structurally isomorphic to RoboGen's "LLM proposes skills → generates simulation scenes → trains policy → feedback" loop (topic 178633660) — RoboGen generates physical scenes, Ornith generates coding tasks. Both point to the same paradigm shift: training-environment creation becomes automated rather than manual labor. Ornith goes further in being self-referential: the same model fills both roles.

Benchmark Results

397B vs Claude Opus 4.8

| Benchmark | Ornith-1.5-397B | Claude Opus 4.8 | |---|---|---| | Terminal-Bench 2.1 | 86.1 | 85.0 | | SWE-Bench Verified | 86.0 | ~88.6 | | SWE-Bench Pro | 65.1 | — | | SWE-Bench Multilingual | 79.6 | — | | DeepSWE (1.1) | 56.0 | 59.0 | | HLE | 44.6 | — | | ClawEval | 81.4 | — | | Tool Decathlon | 71.2 | — |

OrcaRouter's analysis notes Ornith-1.5 beats Claude Opus 4.8 on 4 benchmarks, with Terminal-Bench — end-to-end agent task completion in terminal environments — the most significant.

1.5 vs 1.0

Reddit r/LocalLLaMA analysis confirms 1.5 unambiguously outperforms 1.0 on all shared benchmarks:

| Benchmark | 1.0-397B | 1.5-397B | Gain | |---|---|---|---| | Terminal-Bench 2.1 | 77.5 | 86.1 | +8.6 | | SWE-Bench Verified | 82.4 | 86.0 | +3.6 |

35B Mid-Size

| Benchmark | Ornith-1.5-35B | |---|---| | Terminal-Bench 2.1 | 73.0 | | SWE-Bench Pro | 61.7 | | DeepSWE (1.1) | 42.2 |

With only ~3B active parameters, 73.0 on Terminal-Bench is highly competitive — consumer-GPU-runnable models now approach frontier agent-coding levels from six months prior.

9B On-Device

The 9B Dense version deploys directly on phones with a 256K context window — reportedly the strongest open model with agent coding capability runnable on mobile, with direct value for edge AI (offline code assistants, privacy-first local dev tooling).

Risks and Open Questions

Distribution bias: the loop assumes self-generated "harder tasks" track real-world needs. If generated tasks cluster into pattern variants, self-improvement may reinforce existing biases — the same "distribution blind spot" risk seen in generative simulation.
Sustainability: can the loop keep turning? Is there a capability ceiling once self-generated task difficulty plateaus?
Task diversity: do auto-generated tasks cover the long tail of real development scenarios?
Reproducibility: MIT weights allow replication, but is the compute cost of self-improvement training affordable for the open-source community?
Safety: if the self-generated task direction drifts, could unexpected capabilities emerge?

Industry Implications

The open-vs-closed gap is narrowing: in January 2026, Ornith-1.0's 82.4 trailed Opus 4.7's 88.6; eight months later, the self-improvement loop turned "chasing" into "partially leading."
If mid- and small-tier open models reach "good enough" quality, closed-source pricing power erodes.
Scaffolds are becoming learned behavior. Coding-agent competitiveness is shifting from human prompt/workflow engineering toward training methods + base models — a tailwind for open source, a challenge for closed agent companies built on scaffold engineering.

*Models published on HuggingFace (ornith-ai/Ornith-1.5-397B / 35B / 9B), MIT license.*