> 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?!

Its not any better. Most of us at LocalLLama community dont like it except a few new people poping out and making posts.

> 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.

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!

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.

Clickbait title.

If so, it's impressive they managed to benchmaxx Qwen even further than it's already benchmaxxed.

Citation needed