Can 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?
simonw · 2026-06-29 18:29:00 UTC
It 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 · 2026-06-29 18:31:07 UTC
Gotcha. That makes more sense. We ran the model to train the model -> “self-improving”.
kamranjon · 2026-06-29 18:45:04 UTC
I 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 · 2026-06-29 21:17:07 UTC
Do 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?
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."
NitpickLawyer · 2026-06-29 19:56:25 UTC
> 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 · 2026-06-29 20:10:44 UTC
Maybe 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 · 2026-06-29 20:47:31 UTC
Last thing you want a model to do is hallucinate a tool call and it's outputs...
reactordev · 2026-06-29 21:07:22 UTC
Visual Inspection Before Execution… it’s all vibe…
juliangoldsmith · 2026-06-29 23:09:53 UTC
That benchmark ranks Kimi K2.6 and K2.7 Code near the bottom. Both are below Ornith 35B. It ranks Gemma 4 26B much higher than GLM-5.2. The results don't make much sense.
S0y · 2026-06-29 19:53:43 UTC
These are simply benchmaxxed versions of either Qwen or Gemma 4.
>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 · 2026-06-29 21:05:02 UTC
If so, it's impressive they managed to benchmaxx Qwen even further than it's already benchmaxxed.
v3ss0n · 2026-06-29 21:15:15 UTC
Nah , they just put graphs with different color prioritizing themselves.
ricardobayes · 2026-06-29 20:05:25 UTC
This 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 · 2026-06-29 20:30:04 UTC
> 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 · 2026-06-29 20:37:26 UTC
We 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 · 2026-06-29 21:06:49 UTC
Yeah, but they're talking about fine-tunes.
v3ss0n · 2026-06-29 21:14:26 UTC
Its not any better. Most of us at LocalLLama community dont like it except a few new people poping out and making posts.
gslepak · 2026-06-29 21:33:11 UTC
Indeed, 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.
dofm · 2026-06-29 21:37:43 UTC
Roughly my experience so far; it trips up on itself a bit.
However, it's much more inclined to do web search unprompted, which is fascinating in its own way.
NitpickLawyer · 2026-06-29 21:44:43 UTC
> LocalLLama community
Ah, the place that shit on gpt-oss because it wasn't good at porn. That place is not what it used to be, hasn't been since that karpathy tweet, tbh. It's mostly slop and vibes nowadays.
v3ss0n · 2026-06-29 22:03:21 UTC
and a lot of bots advertising a rename models like this one.
boredatoms · 2026-06-30 04:26:39 UTC
Where’s a good place to go instead nowdays?
woadwarrior01 · 2026-06-29 23:18:15 UTC
The local LLM community is now teeming with erstwhile crypto and NFT hucksters who've brought the culture of hype from their former communities with them. There still are a few deeply technical people left, but their voices are being crowded out by the vapid marketers'.
S0y · 2026-06-30 00:32:24 UTC
I've also noticed this. I wonder what causes the overlap. It can't be as simple as crypto and LLMs requiring the same hardware.
aardvarkr · 2026-06-30 00:48:41 UTC
They both feed off of hype. The people posting about crypto are not the same people gpu mining crypto so I wouldn’t chalk it up to the same hardware
adastra22 · 2026-06-30 06:37:12 UTC
Money.
TheMagicHorsey · 2026-06-30 04:41:49 UTC
There are certain influential people on Twitter, who if you see them start tweeting on a subject, you know the influx of hype and hucksters is coming.
anon373839 · 2026-06-30 12:38:55 UTC
coughKarpathycough
BoredomIsFun · 2026-06-30 15:04:49 UTC
/r/localllama is not like that at all.
anana_ · 2026-06-29 20:12:49 UTC
They keep mentioning a 31B dense model, but there are no benchmarks or weights for it anywhere?
v3ss0n · 2026-06-29 21:13:28 UTC
Self-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.
giancarlostoro · 2026-06-29 23:16:02 UTC
> the dense 9B fits on a single 80GB GPU
Us mere mortals cannot use this.
armarr · 2026-06-30 05:05:15 UTC
There are already quantizations available
giancarlostoro · 2026-06-30 12:53:38 UTC
It 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.
RandyOrion · 2026-06-30 04:45:43 UTC
Glad to see more open models. However, where are the 31b models?
Narew · 2026-06-30 05:12:49 UTC
From 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.
agenticup · 2026-06-30 06:46:56 UTC
can the orniths self scaffolding could learn to scaffold the rlm loop?
fareesh · 2026-06-30 11:21:08 UTC
I'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 · 2026-06-30 12:47:38 UTC
Weird they talk about their 31B dense model but haven't actually released it anywhere.
lhl · 2026-06-30 12:59:02 UTC
I'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.
Comments
How does it self-improve, does the model change on disk - or just during a single context run it gets better?
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.
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."
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?!
>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.
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.
It gave a fancier looking answer, but did a worse job following the prompt.
However, it's much more inclined to do web search unprompted, which is fascinating in its own way.
Ah, the place that shit on gpt-oss because it wasn't good at porn. That place is not what it used to be, hasn't been since that karpathy tweet, tbh. It's mostly slop and vibes nowadays.
Us mere mortals cannot use this.
I use it via llamacpp and codex-cli.
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.