This is the golden age of model training. Some days ago, I decided I wanted a local CPU only model that can perform exceptionally well for English to Bash translation (to avoid the googling for command syntax). I got a bunch of subagents to generate large amount of training data (140k+ samples), got the Qwen 3 0.6B base model, pointed Astra at it, and off to the races. It trained for 2 days (on and off) and I got a surprisingly good model for my task! The total active time I spent was a few hours. And it is still improving, what a time to be alive!
I didn't have a local GPU, so I asked it to go out and find hardware. It found a google TPU v6e which seemed reasonably priced. I gave it my google api key. I told it to use TPU only when training and bring it down afterwards. That's about it.
On the cloud side, nothing valuable existed, so the training couldn't ruin anything it didn't create. On the laptop side, I usually ask the agents to create named scripts for everything it needs to access, then those local script directory is green-lit with approve all. For cost, I kept giving it new budget in the 20-30 dollar increments.
I had to intervene a few times. For instance, as smart as the models are said to be (Astra), it would copy the full training run, train on the server, pull every checkpoint to the local machine, then run tests, update. So, the bandwidth bill was as high as training bill for the first 6 hours. It could have simply tested each checkpoint on the server, saved time and money, didn't occur to it until I said.
I am thinking about opensourcing everything, although this is not my main domain or my main startup, so the overhead of huggingface etc seems a bit unnecessary
Off topic:With sol pricing drop tbh kimi k3’s value prop has not been that great. For our internal use case/testing/benchmarks sol come out with way better quality and much cheaper costs.
Kimi really needs to drop their pricing (I heard it’s set by them across all the neoclouds)
Sol is at 2/10 vs kimi’s 3/15
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
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?
> 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.
I think people make mistake here, google’s approach is not to spend $2.3 on every $1.0 earned, they’re riding on serving to masses “luna”, they absolutely have way more powerful models internally but they don’t clutter their infrastructure with fragile and costly intelligence/size frontier. I think “underdog” perception is illusory/temporary, not stupidity, but calculated, conscious, longer term bet.
Diff people have diff motives to experiment, then new work is done on top of stuff that "hits" in a way no one anticipated. Then work gets piled on top in a way that might make it hard to port
> then new work is done on top of stuff that "hits" in a way no one anticipated.
Indeed. And when you have freedom to play, you are able to find new stepping stones that you didn't anticipate. And you can combine stepping stones in new ways to make new discoveries.
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.
not necessarily, just knowing something is possible will motivate others to achieve it somehow. Which is why there are so many LLMs and OAI doesn't have a monopoly
Over on /r/LocalLLaMA there's a group that's been getting popular doing the same thing for the Qwen 27B (and other) models. - https://huggingface.co/ukisai
This is partly the appeal of Jev et al; having a quick model for simple tasks, that doesn’t require that much thinking
It’s amazing all the workflows that models like that can unlock. And yes, classifiers and other ML models have been around for a while for these types of tasks, but Jev has made it easy and cheap to play and experiment. This in turn, is incentivizing people to try them for a bunch of stuff, unlocking creativity and producing a lot of new cool (and eventually potentially very useful) applications
Lots of use cases!
I've personally used it for the following:
1. Evals (once you have your rubric defined and tuned using a reasoning model, jev can be great for running periodic evals especially those that run daily.
2. e-commerce catalog classification
3. quick search using anything as context and query mapping to a pre-defined set.
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.
Because we do. The GPL isn't a suggestion. If you can take open source code and make private software out of it then what are we all doing? No, license requirements and agreement are law for a reason.
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.
The pareto frontier needs clearer distinction. Benchmarks miss half the story. What, if any, capability is lost by the token reduction (for example, was it like super awesome at Golang before and now kind of sucks? that kind of distinction).
The result? Ember-1 set a new Pareto frontier for Bedside Bench across both open and closed models including GPT-5.6 Sol, GPT-6 Astra, and Claude Opus 5 on cost/task.
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.
On the smaller end, Quen 3.8, while being extraordinarily capable for a small local model, also suffers from extreme thinking. I wonder if the techniques described here generalize to other models too.
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.
This is really interesting. I think the Fireworks Serverless Training infrastructure they used to develop it is also unique and needed. Except if someone works at one of a handful of the largest labs, it is very difficult to set up or try any sort of training pipeline. The managed training infrastructure makes it available to more people.
This is the golden age of model training. Some days ago, I decided I wanted a local CPU only model that can perform exceptionally well for English to Bash translation (to avoid the googling for command syntax). I got a bunch of subagents to generate large amount of training data (140k+ samples), got the Qwen 3 0.6B base model, pointed Astra at it, and off to the races. It trained for 2 days (on and off) and I got a surprisingly good model for my task! The total active time I spent was a few hours. And it is still improving, what a time to be alive!
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.
what hardware are you using to train?
I didn't have a local GPU, so I asked it to go out and find hardware. It found a google TPU v6e which seemed reasonably priced. I gave it my google api key. I told it to use TPU only when training and bring it down afterwards. That's about it.
What kind of observability did you have over this process? I’m interested in how my peers are operating these efforts.
On the cloud side, nothing valuable existed, so the training couldn't ruin anything it didn't create. On the laptop side, I usually ask the agents to create named scripts for everything it needs to access, then those local script directory is green-lit with approve all. For cost, I kept giving it new budget in the 20-30 dollar increments.
I had to intervene a few times. For instance, as smart as the models are said to be (Astra), it would copy the full training run, train on the server, pull every checkpoint to the local machine, then run tests, update. So, the bandwidth bill was as high as training bill for the first 6 hours. It could have simply tested each checkpoint on the server, saved time and money, didn't occur to it until I said.
> I told it to use TPU only when training and bring it down afterwards.
I wouldn't put my house on it. Brave.
Neat!
i also need more info!
I am thinking about opensourcing everything, although this is not my main domain or my main startup, so the overhead of huggingface etc seems a bit unnecessary
just ask the agent to write it up if you don't have time to do a write-up yourself
Off topic:With sol pricing drop tbh kimi k3’s value prop has not been that great. For our internal use case/testing/benchmarks sol come out with way better quality and much cheaper costs. Kimi really needs to drop their pricing (I heard it’s set by them across all the neoclouds) Sol is at 2/10 vs kimi’s 3/15
Competition is good. Without K3/GLM/DS4 etc. there probably would be no reason for OpenAI to drop Sol's price.
Agreed. Even on the open weight side, GLM 5.3 has roughly equivalent performance to Kimi K3 for less than half the 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
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?
> 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.
I think people make mistake here, google’s approach is not to spend $2.3 on every $1.0 earned, they’re riding on serving to masses “luna”, they absolutely have way more powerful models internally but they don’t clutter their infrastructure with fragile and costly intelligence/size frontier. I think “underdog” perception is illusory/temporary, not stupidity, but calculated, conscious, longer term bet.
Diff people have diff motives to experiment, then new work is done on top of stuff that "hits" in a way no one anticipated. Then work gets piled on top in a way that might make it hard to port
> then new work is done on top of stuff that "hits" in a way no one anticipated.
Indeed. And when you have freedom to play, you are able to find new stepping stones that you didn't anticipate. And you can combine stepping stones in new ways to make new discoveries.
Greatness cannot be planned.
No, because close labs/models borrow but don't contribute back.
Won't the "frontier" labs figure out whatever techniques were used and apply them to their closed models?
Like how the last 2 decades of tech companies are thinly veiled open source pilfering into business units.
If they can keep up.
The lock-in is less pronounced as it is with AWS or MS.
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.
not necessarily, just knowing something is possible will motivate others to achieve it somehow. Which is why there are so many LLMs and OAI doesn't have a monopoly
Over on /r/LocalLLaMA there's a group that's been getting popular doing the same thing for the Qwen 27B (and other) models. - https://huggingface.co/ukisai
> The problem: thinking models think too much
This is partly the appeal of Jev et al; having a quick model for simple tasks, that doesn’t require that much thinking
It’s amazing all the workflows that models like that can unlock. And yes, classifiers and other ML models have been around for a while for these types of tasks, but Jev has made it easy and cheap to play and experiment. This in turn, is incentivizing people to try them for a bunch of stuff, unlocking creativity and producing a lot of new cool (and eventually potentially very useful) applications
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.
Lots of use cases! I've personally used it for the following:
1. Evals (once you have your rubric defined and tuned using a reasoning model, jev can be great for running periodic evals especially those that run daily.
2. e-commerce catalog classification 3. quick search using anything as context and query mapping to a pre-defined set.
Why not using a cheap LLM with thinking completely disabled ? I don't think it will be much more expensive than jev.
I’ve tested this with some local LLMs and their accuracy is in general better than Jev/Laya, but they are super slow in comparison as well
For example, a typical/stock LLM can’t really play Doom in real time, but a Jev-like model can. Just because of latency
Of course, if you want the best Doom player, there are way better and faster adhoc models
> The problem: thinking models think too much
Analysis paralysis stifles not just human intelligence, but other intelligences too.
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.
So they trained a model on open weights, and then aren't releasing the weights... am I reading this right?
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.
weight available
It happens. Most open licenses aren't GPL style copyleft.
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.
Because we do. The GPL isn't a suggestion. If you can take open source code and make private software out of it then what are we all doing? No, license requirements and agreement are law for a reason.
Kimi K3 has its own license which is permissive, it isn't at all like GPL https://github.com/MoonshotAI/Kimi-K3/blob/main/LICENSE
Because the licenses that apply to software make no sense in the context of LLMs. With the latter, there is no source code to license.
The words of a license are what the license is.
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.
Well done, and great iteration.
The pareto frontier needs clearer distinction. Benchmarks miss half the story. What, if any, capability is lost by the token reduction (for example, was it like super awesome at Golang before and now kind of sucks? that kind of distinction).
Unfortunately, it’s hard to make a chart of that.
Need this done for DeepSeek, ideally one of the Flash models.
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.
Does anybody know if this would be a good model for creative writing?
> model
> creative
Choose one.
Do they mean Opus 5.5 or Opus 5?
The result? Ember-1 set a new Pareto frontier for Bedside Bench across both open and closed models including GPT-5.6 Sol, GPT-6 Astra, and Claude Opus 5 on cost/task.
"Pareto": 8 hits
"Opus 5.5": zero hits
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.
On the smaller end, Quen 3.8, while being extraordinarily capable for a small local model, also suffers from extreme thinking. I wonder if the techniques described here generalize to other models too.
https://www.reddit.com/r/LocalLLaMA/comments/1wj3s31/thank_y...
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.
I don't think the article mentions Pareto frontier enough.
Also, did I miss a memo? Suddenly every article on AI seems to be talking about the Pareto frontier - or have I just not been paying attention?
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.
They want it to be the best at something. And it's obviously not the absolute smartest. So here we are.
This is really interesting. I think the Fireworks Serverless Training infrastructure they used to develop it is also unique and needed. Except if someone works at one of a handful of the largest labs, it is very difficult to set up or try any sort of training pipeline. The managed training infrastructure makes it available to more people.
I can’t help but think it’s more expensive tinker.
It looks like it would be similar to GLM 5.3 Flash, had they tested it...