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Did your Astra do any RL or just SFT? did it make up any benchmark to ensure the fine-tuning was a success?

I don't understand. If you have a model that can do bash examples already (your subagents), then why would you need to train a model?

Or are the subagents generating your training data using a closed/paid model?

Curious about how you generated the training data? Was it just asking an existing model to generate a bunch of examples?

I ask cause would this be a kind of model distillation?

I have a small model I'm looking to train on some data, and I have some real live data but I'd love to be able to extend it.

If it’s one of thing that you want just for English to bash shell commands, I will create AST, it is deterministic, exceptionally fast, no tokens so no need to fine tune existing model, please let me know your thoughts.

That's a really impressive result. There are all kinds of small tasks like this I use an LLM for, but theoretically if you broke all the sub-use cases into local-only models, and had something lightweight that routed to the right model, you could have faster and cheaper workflows. E.g. something trained on the linux man pages for common commands, since it's usually quicker to ask an LLM for a specific command with flags than to consult the man pages.

Seriously, I'm using a Qwen 3.8 27B on the homelab, distilled from supposed Fable traces. Regardless, the difference is notable, less thinking, better output. Distilled / heavy quant is better than the original (imv)

https://huggingface.co/vwdubb/Qwen3.8-27B-Fable-Distill-NVFP...

side quest, are fable distillations only wrong when it's another country?

Golden age before the age that ends humanity. Not talking about any "rogue AI", just the known statistical models of what is coming due to climate change.

> what a time to be alive!

It's good to hear you're enjoying yourself, but I suggest retiring that expression. It's really beginning to grate.

Sorry for the aside, but I noticed half the usecase of AI is fixing the awful DX.

what hardware are you using to train?

i also need more info!

Just read the terms of service and read this blog post and I think your concern will be addressed.

That also could explain why openrouter is worth that much

Agreed. Even on the open weight side, GLM 5.3 has roughly equivalent performance to Kimi K3 for less than half the cost.

Competition is good. Without K3/GLM/DS4 etc. there would be no pressure on OpenAI to drop Sol's price.

With DeepSeek's pricing, no other value prop has been great.

Sol pricing dropped but so did the quality few days ago. I wonder when these companies are sued for making the terms from their side to go downwards while taking the same subscription cost.

Agreed, I think the only place where it’s still interesting is ui design. Visually kimi and muse feel much nicer than frontier models to me, but maybe it’s an artifact of everything terrible being Claude Design

I was surprised by that. I run my benchmark [1] every couple of days and was sure this model will be ath the pareto frontier, if not THE pareto frontier. But no:

Ember isn't picked yet. In planning, Opus 5.5 wins under the planning weights. In code, GPT-6 Sol dominates it: also 10/10, but with a higher quality score and a lower estimated cost. Ember has no intelligence index, so its starting score is only 0.73, which holds its 10/10 down to 0.954 against Sol's 0.975.

[1] https://philippdubach.com/posts/jev-model-router-for-pi/

> With sol pricing drop

6 or 5.6? Because 6 is hot garbage

> Ignoring for the moment issues of what "counts" as open, won't open models rapidly advance due to stuff like this in ways that it's less possible for the proprietary ones to do? This is exactly how Linux & Wikipedia, for example, overtook their "frontiers", right?

I suspect the advantage that catapulted Linux ahead of the establishment was less technical potential and talent and more organizational advantage. That's not to diminish the technical talent of the Linux crew, but them being unencumbered gave them more degrees of freedom. The rest is history.

So as long as the AI companies don't succumb to "big company" dynamics, they can outlead. To wit: Open AI and Anthropic are kicking Google's ass.

No, because close labs/models borrow but don't contribute back.

This was exactly the crux of Nathan Lambert's recent testimony to a group of US Congressional members/staff: https://www.interconnects.ai/p/the-current-balance-of-power-...

Won't the "frontier" labs figure out whatever techniques were used and apply them to their closed models?

The difference between contributing to OS and AI, is that the first is a hobby alternative to woodworking or hiking, while the other can easily bootstrap you a company you can get millions in investment, at least for time being.

Yes, absolutely, but only if people keep contributing in the open.

> to what end?

I'd expect that their business strategy is to compete in more markets, and if successful, they can capture more value. This is the "easiest" for them as they already have GPUs, a training environment etc. For that platform it's not the worst if there's an internal customer team that can help shape the future and provide immediate feedback, and if it results in a good model, even better.

Other things I'd not be surprised they offer in the future in the same vein: A multi-model harness, coding agent (cloud and local), and maybe at a later point in time even a CPU-only cloud compute product.

The end: make lots of money. The means: systematically take existing reasoning models, do some more post-training of some sort to make them achieve the same outputs with less reasoning tokens (ie, cheaper). Same quality but cheaper is always valuable.

It's unclear if they can do this systematically and it's unclear if they can do it better than others. But, lots of things are unclear in AI at the moment, this doesn't seem outrageous on the surface. And, it could just be marketing. And it could be the first option with the backup of the second.

I think they are trying to show potential customers what is possible.

Vertical integration.

What are the useful applications of Jev so far? Not to sound dismissive, I just haven’t seen what people are using it for yet.

Why not using a cheap LLM with thinking completely disabled ? I don't think it will be much more expensive than jev.

The thinking traces on some Chinese models just output the full response in the thinking trace, then output it again to the user, which is redundant.

Yes and thar makes you wonder if the Paradox of Choice would apply as well ;)

The more options you have, the harder it becomes to be satisfied with the one you picked.

Technically kimi k-3 weights license is not open weight (it has a lot of restrictions). I would classify it as ‘weight open’ similar to the bsl and fsl ’source open’ licenses.

It happens. Most open licenses aren't GPL style copyleft.

There is little to no point reading the article as well. It's stripped of all alpha.

> task and environment feedback

> on-policy planning and learning

> feedback connects decisions to their consequences

These are deliberately the least informative phrases you could possibly use to describe what you have done, while still being in the realm of words that go over a generic investor who has no idea whats going on and may be dazzled by sciencey sounding language.

Cursor compose 2.5 article where they used and described on policy self distilation was actual alpha.

Aren't Cursor Composer models like this too? At some point all the extra RL you do can be considered as proprietary information added.

Not suggesting this is right or wrong, but is sort of the nature of the technology.

It happens with open source software all the time, why would we expect any different with open source weights.

Which is fine, that’s legal according to the license

I don't see why? It is a well defined term that existed prior to the recent AI bubble/revolution, and from what I can see they are using it appropriately.

Unfortunately, it’s hard to make a chart of that.

Why is proprietary-licensed software unethical?

Kimi K3 itself isn't FOSS. Speaking of reciprocity: Fireworks is presumably paying Moonshot serious money for the right to do what they are doing here, since Kimi's license[0] excludes commercial inference providers (such as Fireworks) from gratis use. It requires them to: "...enter into a separate agreement with Moonshot AI before using the Software or its derivative works..."

[0] https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE#...

> Is It better a model that takes me to 90% in 1 dollar or one that takes me to 95% in 2 dollars?

It's pretty important to understand if your own work domain is one where the last 5% matters. In a lot of day-to-day software engineering tasks, it doesn't, and one can get crazy mileage out of the cheaper models. OTOH, if you are performing novel research, that last 5% may be worth whatever it costs...

And GLM. Both Deepseek 4.1 Flash and GLM 5.3 Flash are quote verbose when thinking.

If you have the compute, I have the expertise.

> model

> creative

Choose one.

Obviously this research was done before 6.0 Sol and Opus 5.5 came out. Your point stands that the frontier moves quickly and small gains can be eclipsed quickly.

I suspect it might generalize to other large models, but I don't think Qwen3.8 27B is one of them. Kimi K3 is a 2.8 trillion parameter model, and I suspect that is playing a big role in being able to reduce the length of CoT without taking a hit in quality.

That's just vibes, though.

https://www.reddit.com/r/LocalLLaMA/comments/1wj3s31/thank_y...

Pareto frontier on some benchmark that I am hearing of for the first time.

Kimi K3 with less reasoning tokens isn't exactly exciting either, and particularly so if the license is less open than original Kimi K3.

I guess they figure "best bang for your buck" comes off a little too colloquial.

Is there a Pareto frontier for the number of times articles mention or don't mention a Pareto frontier.

They want it to be the best at something. And it's obviously not the absolute smartest. So here we are.

when everyones fighting to be 'somewhere in the pile' they need some way to advertise they have made progress while not being the best.

I can’t help but think it’s more expensive tinker.