DeepReinforce Releases Ornith-1.0: An Open-Source Coding Model Family That Learns Its Own RL Scaffolds
DeepReinforce released Ornith-1.0, an open-source coding model family built on Gemma 4 and Qwen 3.5. Instead of a fixed harness, the model learns its own scaffold during reinforcement learning. The 397B flagship reports 82.4 on SWE-Bench Verified, with all weights under the MIT license. The post DeepReinforce Releases Ornith-1.0: An Open-Source Coding Model Family That Learns Its Own RL Scaffolds appeared first on MarkTechPost .
DeepReinforce has released Ornith-1.0, an open-source model family built for agentic coding. The lineup spans four sizes, from a 9B dense model to a 397B mixture-of-experts flagship. Every checkpoint ships under the MIT license on Hugging Face. The models are post-trained on top of pretrained Gemma 4 and Qwen 3.5.
Most coding agents pair a model with a fixed, human-designed harness. Ornith-1.0 instead learns to write its own. The DeepReinforce research team reports state-of-the-art results among open models of comparable size.
• Ornith-1.0 ships in 9B, 31B, 35B-MoE, and 397B-MoE sizes under MIT, built on Gemma 4 and Qwen 3.5.
• The model learns its own scaffold during RL, jointly optimizing the harness and the solution.
• Ornith-1.0-397B tops Claude Opus 4.7 on both headline benchmarks, but not Opus 4.8 or the larger GLM-5.2-744B.
• Three layers — fixed trust boundary, deterministic monitor, frozen LLM judge — guard against reward hacking.
What is Ornith-1.0?
Ornith-1.0 is a set of reasoning models tuned for coding agents. The variants are 9B Dense, 31B Dense, 35B MoE, and 397B MoE. The 35B model is mixture-of-experts and activates roughly 3B parameters per token. FP8 and GGUF builds are also published for faster local serving.
Each model is a reasoning model. Replies open with a
Deployment is straightforward. The 9B model is about 19GB in bf16 and serves on a single 80GB GPU. Serving recipes target vLLM, SGLang, and Transformers. Each model exposes an OpenAI-compatible endpoint. Standard agent frameworks therefore work without code changes.
Interactive Explainer
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'Baseline harness: linear retries, no memory.',
'Adds scratchpad memory across tool calls.',
'Adds error-triage branch before re-edit.',
'Reorders: read tests, then plan, then patch.',
'Caches sub-results; prunes dead branches.',
'Task-specific orchestration emerges automatically.'];
'Fixed harness, no learning yet.',
'Fewer redundant file reads observed.',
'Recovers from failed edits more often.',
'Higher first-pass test success.',
'Shorter trajectories, same accuracy.',
'Stable high-reward scaffold selected.'];
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t397:{label:'Ornith-1.0-397B',hero:'Ornith-1.0-397B',
models:['Ornith-1.0-397B','Qwen3.5-397B','Qwen3.7-Max','GLM-5.2-744B','Minimax-M3-428B','DeepSeek-V4-Pro-1.6T','Claude Opus 4.7','Claude Opus 4.8'],
vals:[[77.5,53.5,73.5,81.0,64,64,70.3,85],[82.4,76.4,80.4,null,null,80.6,80.8,87.6],[62.2,51.6,60.6,62.1,59,55.4,64.3,69.2],[78.9,69.3,78.3,null,null,76.2,null,null],[48.2,36.8,47.2,48.9,42.1,null,null,69.7],[77.1,70.7,65.2,null,null,75.8,78.2,null]]},
t35:{label:'Ornith-1.0-35B-A3B',hero:'Ornith-1.0-35B-A3B',
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t9:{label:'Ornith-1.0-9B',hero:'Ornith-1.0-9B',
models:['Ornith-1.0-9B','Qwen3.5-9B','Qwen3.5-35B-A3B','Gemma4-12B','Gemma4-31B'],
vals:[[43.1,21.3,41.4,21,42.1],[69.4,53.2,70,44.2,52],[42.9,31.3,44.6,27.6,35.7],[52,39.7,60.3,32.5,51.7],[27.2,16.2,20.5,10.3,15.5],[63.1,53.2,65.4,32.5,48.5]]}
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The Self-Scaffolding Idea
Most coding agents rely on a scaffold, also called a harness. A scaffold wraps the model with memory, tools, error handling, and orchestration logic. AI teams usually hand-design one scaffold per task category.
Ornith-1.0 treats the scaffold as a learnable object instead. During reinforcement learning, the scaffold co-evolves with the model’s policy. Each RL step runs in two stages.
First, the model reads the task and its previous scaffold. It then proposes a refined scaffold. Second, it uses that scaffold and the task to generate a solution rollout. Reward from the rollout flows back to both stages.
So the model is optimized to author orchestration, not just answers. Over training, higher-reward scaffolds are mutated and selected automatically. Per-task strategies emerge without hand-engineered harness design.
Training also runs asynchronously, using a pipeline-RL setup. A staleness weight downweights older, off-policy tokens and drops them past a threshold. The optimization uses a token-level GRPO objective.
Guarding Against Reward Hacking
Letting a model write its own scaffold invites reward hacking. A scaffold could read visible test files and hardcode expected outputs. It could also copy an oracle solution sitting in the environment. DeepReinforce team describes three defense layers.
• The outer trust boundary is fixed and immutable. The environment, tool surface, and test isolation stay outside the model’s reach. The model evolves only its inner policy scaffold.
• A deterministic monitor flags banned actions. Reading withheld paths or editing verification scripts earns zero reward. Those trajectories are excluded from the advantage computation.
• A frozen LLM judge acts as a veto. It sits on top of the verifier, not as the primary reward.
DeepReinforce reports vendor numbers across several agentic coding benchmarks. At flagship scale, Ornith-1.0-397B posts 77.5 on Terminal-Bench 2.1 and 82.4 on SWE-Bench Verified. On SWE-Bench Verified, that 82.4 trails only Claude Opus 4.8 (87.6) among the listed models. On Terminal-Bench 2.1, the picture is more mixed.
Ornith-1.0-397B beats Claude Opus 4.7 (70.3) on Terminal-Bench 2.1. But it trails Claude Opus 4.8 (85) and the larger GLM-5.2-744B (81.0). So the ‘state-of-the-art’ claim is scoped to open models of comparable size.
The smaller models carry the efficiency case. The 35B model scores 64.2 on Terminal-Bench 2.1, above Qwen 3.5-397B’s 53.5. The 9B model reaches 43.1 on Terminal-Bench 2.1 and 69.4 on SWE-Bench Verified.
BenchmarkOrnith-1.0-397BQwen3.5-397BQwen3.7-MaxGLM-5.2-744BMinimax-M3-428BDeepSeek-V4-Pro-1.6TClaude Opus 4.7Claude Opus 4.8Terminal-Bench 2.177.553.573.581.0646470.385SWE-Bench Verified82.476.480.4––80.680.887.6SWE-Bench Pro62.251.660.662.15955.464.369.2SWE-Bench Multilingual78.969.378.3––76.2––NL2Repo48.236.847.248.942.1––69.7ClawEval Avg77.170.765.2––75.878.2–
Use Cases and a Quick Start
The models target terminal-native coding agents and repository-scale work. Practical fits include multi-file refactors, bug localization, and test-driven patches. The 9B model suits edge or single-GPU setups where latency and cost matter. The 397B model targets maximum accuracy on long, multi-step tasks.
For example, a dev can run the 9B model locally to triage a failing test suite. A platform team can self-host the 397B model for an internal coding agent.
Serving is a one-liner with vLLM:
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vllm serve deepreinforce-ai/Ornith-1.0-9B \
--served-model-name Ornith-1.0-9B \
--max-model-len 262144 \
--enable-auto-tool-choice --tool-call-parser qwen3_xml \
--reasoning-parser qwen3 \
--trust-remote-code
Then call it with any OpenAI client:
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from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
resp = client.chat.completions.create(
model="Ornith-1.0-9B",
messages=[{"role": "user", "content": "Write a Python is_prime(n)."}],
temperature=0.6, top_p=0.95,
msg = resp.choices[0].message
print(getattr(msg, "reasoning_content", None)) # the
print(msg.content) # the final answer
The reasoning trace returns in reasoning_content, with the answer in content. Recommended sampling is temperature=0.6, top_p=0.95, top_k=20. The model also plugs into OpenHands, OpenClaw, and OpenCode.
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The post DeepReinforce Releases Ornith-1.0: An Open-Source Coding Model Family That Learns Its Own RL Scaffolds appeared first on MarkTechPost.
