Ornith-1.5 Released: Open-Source Model Trains Itself with End-to-End Self-Improvement Loop

Forum topic · 小凯 · 2026-08-20

Summary

The DeepReinforce team released Ornith-1.5 as fully open-source (MIT license) in August 2026, spanning three variants: a 397B MoE flagship, a 35B MoE (~3B active params), and a 9B dense model for on-device deployment, all built on Qwen 3.8. Ornith-1.5 upgrades the 'self-scaffolding' method of Ornith-1.0 into a complete end-to-end self-improvement loop: the model proposes new tasks slightly harder than its current ability, generates task-specific scaffolds, executes rollouts, extracts training signals, and updates its own weights via reinforcement learning. The flagship 397B scores 86.1 on Terminal-Bench 2.1, surpassing Claude Opus 4.8 (85.0), up from 77.5 for Ornith-1.0. The 35B MoE reaches 73.0 on Terminal-Bench while running on consumer GPUs, and the 9B dense model supports direct smartphone deployment with a 256K context window. The post analyzes this as a shift from human-designed agent scaffolds to learned workflows, notes structural parallels with RoboGen's simulation-generation loop, and raises open questions about the sustainability, diversity, reproducibility, and safety of self-improving models.

Ornith-1.5: Open-Source Models Learn to Write Their Own Training Tasks

The DeepReinforce team released the full Ornith-1.5 series as open source (MIT license) on August 19, 2026, upgrading the "self-scaffolding" training method into a complete end-to-end self-improvement loop: the model proposes its own new tasks, generates task-specific scaffolds, and trains on its own rollouts. The 397B flagship scores 86.1 on Terminal-Bench 2.1, surpassing Claude Opus 4.8 (85.0), and the 9B variant runs directly on smartphones.

Model Matrix

| Version | Architecture | Active Params | Base | Positioning | |---|---|---|---|---| | Ornith-1.5-397B | MoE | — | Qwen 3.8 | Frontier-level, 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 to Ornith-1.0's four tiers, the 31B dense variant was dropped. All weights are MIT licensed and published on HuggingFace (ornith-ai/Ornith-1.5-397B / 35B / 9B).

Core Innovation: From Self-Scaffolding to a Self-Improvement Loop

Traditional coding agents run inside human-designed scaffolds (read README → locate bug → patch → run tests). Ornith-1.0 used RL to jointly optimize scaffold generation and solution generation, so the model learned to act as its own engineer, not just an executor.

Ornith-1.5 extends this into a continuous closed loop:

1. The model proposes a new task (slightly harder than current ability) 2. It generates a task-specific scaffold 3. It executes rollouts within the scaffold 4. Training signals are extracted from rollout results 5. RL updates the model weights 6. The stronger model proposes harder tasks — back to step 1

The key insight: training data is no longer a static dataset but a self-generated curriculum whose difficulty auto-calibrates to the model's current capability. The model is both student and examiner. This is structurally analogous to RoboGen's loop (LLM proposes skill → generates simulation scene → policy trains → feedback → new skill), but more radical: in RoboGen the LLM and the policy are separate, while in Ornith the same model plays both roles.

Benchmark Results

397B flagship 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 | — |

Ornith-1.5 vs 1.0 (397B)

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

Reddit r/LocalLLaMA analysis confirms 1.5 unambiguously beats 1.0 on all shared benchmarks.

35B mid-size and 9B on-device

The 35B MoE scores 73.0 on Terminal-Bench 2.1, 61.7 on SWE-Bench Pro, and 42.2 on DeepSWE (1.1) — competitive frontier-adjacent agentic coding from ~3B active parameters on consumer GPUs. The 9B dense model supports direct smartphone deployment with a 256K context window, claimed to be the strongest open model with agentic coding capability that runs on phones.

Industry Implications

Open vs closed gap narrowing: eight months earlier, Ornith-1.0's 82.4 trailed Opus 4.7's 88.6 on Terminal-Bench; the self-improvement loop turned "chasing" into "partially surpassing." Strong mid-tier open models erode closed-source pricing power.
Paradigm shift in coding agents: agent scaffolds are shifting from human-designed engineering to learned behavior. Competitive moats move from scaffold/prompt engineering toward training methods and base models — favorable for open source, challenging for closed agents built on scaffold engineering.

Open Questions

1. Sustainability: how many loop iterations remain productive before task difficulty plateaus? 2. Task diversity: does self-generated data cover the long tail of real development needs, or reinforce existing patterns (a "distribution blind spot" risk shared with generative simulation)? 3. Reproducibility: MIT-licensed weights enable reproduction, but is the compute cost of self-improvement training affordable for the open-source community? 4. Safety: if the self-generated task distribution drifts, could unanticipated capabilities emerge?