41–50 of 99 posts
One question I was wondering about regarding the open models released by big labs is how much more the could improve with additional training. GPT-OSS has 2.1m hours of training, how much score improvements could we see at double that?
As we saw with GPT-5 the RL technique of training doesn't scale forever
From my experience, qwen3-coder is way better. I only have gpt-oss:20b installed to make a few more tests but I give it a program to make a summary of what it does and qwen3 just works in a few seconds, while gpt-oss was cancelled after 5 minuts... doing nothing. So I just use qwen3. Fast and great ouput. If for some reason I don't get what I need, I might use search engines or Perplexity. I have a 10GB 3080 and Ryze…
Qwen3 coder 480B is quite good and on par with Sonnet 4. It’s the first time I realized the Chinese models are probably going to eclipse US-based models pretty soon, at least for coding.
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The strategy of Phi isn't bad, it's just not general. It's really a model that's meant to be fine tuned, but unfortunately fine tuning tends to shit on RL'd behavior, so it ended up not being that useful. If someone made a Phi style model with an architecture that was designed to take knowledge adapters/experts (i.e. small MoE model designed to get separately trained networks plugged into them with routing updates vi…
The Phi strategy is bad. It results in very bad models that are useless in production, while gaming the benchmark to appear like it is actually able to do something. This is objectively bad.
I find it interesting that the architectures of modern open weight LLMs are so similar, and that most innovation seems to be happening on the training (data, RL) front. This is contrary to what I've seen in a large ML shop, where architectural tuning was king.
My guess is that at LLM scale, you really can't try to hyperparameter tune — it's just too expensive. You probably have to do some basic testing of different architectures, settle on one, and then figure out how to make best use of it (data and RL).
This article really goes into a lot of detail which is nice. gpt-oss is just not good for agentic use in my observation. tldr; I'll save you a lot of time trying things out for yourself. If you are on a >=32 GB Mac download LMStudio and then the `qwen3-coder-30b-a3b-instruct-mlx@5bit` model. It uses ~20 GB of RAM so a 32GB machine is plenty. Set it up with opencode [1] and you're off to the races! It has great tool c…
One question I was wondering about regarding the open models released by big labs is how much more the could improve with additional training. GPT-OSS has 2.1m hours of training, how much score improvements could we see at double that?
Earlier quoted context omitted.
The Phi strategy is bad. It results in very bad models that are useless in production, while gaming the benchmark to appear like it is actually able to do something. This is objectively bad.
I like the idea of having a _HIGHLY_ unopinionated base model that's just good at basic logic and instruction following that I can fine tune to my use case. Sadly, full fine tuning tends to make models derpy, and LoRAs are limited in terms of what they can achieve.
Was able to create a sample page, tried starting a server, recognising a leftover server was running, killing it (and forced a prompt for my permission), retrying and finding out it's ip for me to open in the browser.
This isn't a demo anymore. That's actually very useful help for interns/juniors already.
Chrome latest on Ubuntu.
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If I had to make a guess, I'd say this has much, much less to do with the architecture and far more to do with the data and training pipeline. Many have speculated that gpt-oss has adopted a Phi-like synthetic-only dataset and focused mostly on gaming metrics, and I've found the evidence so far to be sufficiently compelling.
That would be interesting. I've been a bit sceptical of the entire strategy from the beginning. If oss was actually as good as o3 mini and in some cases o4 mini outside benchmarks, that would undermine openai's api offer for gpt 5 nano and maybe mini too. Edit: found this analysis, it's on the HN frontpage right now > this thing is clearly trained via RL to think and solve tasks for specific reasoning benchmarks. not…