Why do only big ML labs dominate widely-used models despite many open-source pretrained models smaller labs could do RL on? [D]

Reddit r/MachineLearning / 4/27/2026

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The author questions why widely used models from major ML labs (e.g., GPT, Claude) dominate in real-world usage when similarly scaled open-source pretrained models already exist.
They argue that pretraining compute cost alone may not explain the gap, since some open models (like Kimi) appear to match the scale of closed systems.
The post hypothesizes that the differentiator is the reinforcement learning layer on top of pretraining (e.g., RLHF), and that this step should be more feasible for smaller labs.
Overall, the discussion frames a causal puzzle about how RLHF access, not just pretraining, affects model quality and adoption.

I’m trying to understand why models from major labs (GPT, Claude, etc.) dominate real-world usage? You might say it's due to the expensive pretraining compute budge, but there already exists many pretrained open-source models at the same scale (e.g., Kimi).

Of course Kimi isn't as good as Claude, but it's the RL on top of the pretraining that makes Claude what it is right? Given Kimi, DeepSeek etc all have the expensive pretraining done, the RLHF on top is what makes Claude what it is right? And that should be much more accessible in terms of cost to smaller labs no?

submitted by /u/boringblobking
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