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Ornith-1.0: self-improving open-source models for agentic coding
- anana_ 3mo agoThey keep mentioning a 31B dense model, but there are no benchmarks or weights for it anywhere?
- kennywinker 3mo agoCan anyone explain what’s the story here? Is this just a re-skinned qwen? Who is deepreinforce-ai and why isn’t this model listed on their website? How does it self-improve, does the model change on disk - or just during a single context run it gets better?
- deleted 3mo ago[deleted]
- simonw 3mo agoIt doesn't self-improve, that's a misleading headline. As far as I can tell they trained it by running their own reinforcement learning on top of Qwen and Gemma 4 (not sure how they combined weights from both, or if they used Qwen as the basis and Gemma 4 to help train?) - so the "self-improving" is about their training process, not how you use the weights.
- kennywinker 3mo agoGotcha. That makes more sense. We ran the model to train the model -> “self-improving”.
- deleted 3mo ago[deleted]
- kamranjon 3mo agoI think the 9b and 31b dense are Gemma models and the 35B-MoE, and 397B-MoE are Qwen models since these are model sizes covered by each of them respectively
- sisve 3mo agoDo you think we will get a self-improving model in 26 or 27? Maybe not a native one but some kind of hack so a model will learn something without loosing part of the context window?
- v3ss0n 3mo agoClickbait title.
- CharlesW 3mo agoPreviously: https://news.ycombinator.com/item?id=48709744 https://news.ycombinator.com/item?id=48709744 https://swelljoe.com/post/will-it-mythos/ https://swelljoe.com/post/will-it-mythos/: "Poor performer here, only found the one bug that almost every model found, despite its performance on other benchmarks being excellent for its size. […] It also performs poorly in a chat without tools, exhibiting an ehthusiasm for hallucination. I’m currently working on a replication of this with full tool access, including bash/Python, which may allow this model to be competitive."
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- NitpickLawyer 3mo ago> It also performs poorly in a chat without tools, exhibiting an ehthusiasm for hallucination. I’m currently working on a replication of this with full tool access, including bash/Python, which may allow this model to be competitive. How is that a serious phrase in '26? I mean I have no idea if this fine-tune is good, haven't tried it, but testing a (clearly) agentic model without tool access and expecting it to work is crazy, no? What was he even testing?!
- vikingcat 3mo agoMaybe expecting it to recognize it's limitation without tools instead of hallucinate. But yeah, not wholly useful. It's performance (and proclivity to hallucinations) with tools is what really matters.
- nodja 3mo agoLast thing you want a model to do is hallucinate a tool call and it's outputs...
- reactordev 3mo agoVisual Inspection Before Execution… it’s all vibe…
- juliangoldsmith 3mo ago
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- S0y 3mo agoThese are simply benchmaxxed versions of either Qwen or Gemma 4.
- jorisw 3mo agoCitation needed
- S0y 3mo agoSure. https://deep-reinforce.com/ornith_1_0.html https://deep-reinforce.com/ornith_1_0.html >Built on top of pretrained Gemma 4 and Qwen 3.5, it achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks. >Ornith-1.0 is a self-improving training framework. Instead of relying on human-designed harnesses to drive solution generation in RL, Ornith-1.0 learns to generate both solution rollouts and the task-specific harnesses that guide those rollouts.
- 2001zhaozhao 3mo agoIf so, it's impressive they managed to benchmaxx Qwen even further than it's already benchmaxxed.
- v3ss0n 3mo agoNah , they just put graphs with different color prioritizing themselves.
- ricardobayes 3mo agoThis is the first Qwen fine-tune that is not immediately rejected by the local LLM community, and in some cases even being recommended. Based on my limited usage, it is good, gives creative solutions to coding problems. I don't expect 9-35B models to one-click create full apps. Most people who were complaining did so .
- monkmartinez 3mo ago> Most people who were complaining did so . It has been this way since the beginning, unfortunately. There is certainly no harm in trying on local models on local workloads with modest guardrails. Like most of these models (Qwen, Gemma, Llama, gpt-oss), finding all the little gotchas like, special tokens and prompt structure, model preference are a PITA right now. The reward are really nice models that run exceptionally well in agentic harnesses tuned with the prompts and parameters you fought so hard to learn.
- arcanemachiner 3mo agoWe must be in different communities... Qwen models are the most recommended ones that will actually run on local hardware that is accessible to the masses!
- montroser 3mo agoYeah, but they're talking about fine-tunes.
- v3ss0n 3mo agoIts not any better. Most of us at LocalLLama community dont like it except a few new people poping out and making posts.
- gslepak 3mo agoIndeed, it performed worse than Qwen3.6-27b in my basic test. It gave a fancier looking answer, but did a worse job following the prompt.
- fratefritto 3mo ago[flagged]
- v3ss0n 3mo agoSelf-Improving bullshit. It is just Qwen 3.5 finetune benchmaxxed . Nothing spectacular . even fails at benchmarks. Long session tool calls sucks and hallucinate a lot with that too. Just use Qwen 3.6 and 3.5 122b.
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- giancarlostoro 3mo ago> the dense 9B fits on a single 80GB GPU Us mere mortals cannot use this.
- armarr 3mo agoThere are already quantizations available
- giancarlostoro 3mo agoIt would be nice to run a model that isn't quantized to death so it fits in 12GB of VRAM so I have room for reasonable context window, but also, this is ONE model in a set of models, the rest of the models need to run in a GPU cluster apparently.
- regularfry 3mo agoSeems weird. A 9B model would normally fit unquantised on a 24GB GPU.
- RandyOrion 3mo agoGlad to see more open models. However, where are the 31b models?
- Narew 3mo agoFrom what I personally tested Ornith-1.0 35B is slightly better than Qwen-3.6 35B. My tests are tasks that consist of adding/modify feature in a big C++ codebase. The part that I find interesting is that the model is way faster than Qwen3.6 35B. It seems Ornith produce a smaller chain of thought. On my test it can be 3 time faster to produce the answer. I use it via llamacpp and codex-cli.
- modgate 3mo ago[flagged]
- agenticup 3mo agocan the orniths self scaffolding could learn to scaffold the rlm loop?
- jkwang 3mo ago[flagged]
- seanxx 3mo ago[flagged]
- fareesh 3mo agoI've used a lot of local models and all of them felt like toys. This one actually felt useful. I hear Qwen 36-A3B is also good, yet to try that one.
- smcleod 3mo agoWeird they talk about their 31B dense model but haven't actually released it anywhere.
- lhl 3mo agoI've been testing Ornith-1.0 35B (my own FP8-block quant) and I like it. It runs at >200 tok/s w/ vLLM on an RTX PRO 6000 (sm120), I've run >140M cached tokens of agentic coding work on it over the past few days. It seems to about somewhere between Qwen 3.6 35B-A3B and 27B, but the good thing: it overthinks/doom-loop a lot less than Qwen 3.6. When looking at the thinking traces I like its breakdown approach template. It does good job on basic analysis, tasks, and some front-end/backend changes on a medium-sized Go codebase, but it reached its limits totally botching a longer (simple) kernel implementation job (about 100 iterations in Pi Agent harness) - this is the type of thing that stronger open models (Kimi K2.6, GLM 5.2) are able to do.
- regularfry 3mo agoWith this model size I've found that the harness seems to matter more. I've moved on to little-coder rather than raw pi with qwen3.6 27b personally, it might be worth taking a look.
- lhl 3mo agoYou got me curious, so I made a little harness comparison to my model test suite: Model Adapter Suite Score Passed Tasks --------------------------------- ------------ -------------- ------ ------ ----- local/ornith-1.0-35b little_coder aider_polyglot 36.0% 81/225 225 local/ornith-1.0-35b pi_devstack aider_polyglot 39.6% 89/225 225 local/ornith-1.0-35b pi_vanilla aider_polyglot 32.0% 72/225 225 Little Code does a little better than raw Pi, although maybe not better than my personal Pi setup: https://github.com/lhl/devstack https://github.com/lhl/devstack
- 1105714 3mo ago[flagged]
- GenseeAI 3mo agoSelf-improving systems are exciting, but they also make provenance and governance much harder. Once agents can modify their own behavior over time, understanding why an agent behaved a certain way becomes increasingly important.
- Iris595 2mo agoAgree! More and more industry news also raises concerns about how to regulate agent behavior. Any take on possible actions?
- LoveMortuus 3mo agoWhy don't these 'self-improving' ones eventually improve to the point of being better that the bleeding edge?
- modgate 3mo ago[dead]