I disagree. Sure let them play and see if they can improve. But this model has more compute and more training data than the predecessors it fails to surpass. That only means their training regime is inferior if their predecessors did so much more with so much less. That inferiority should not be encouraged.
replyThe reality is they trained a model and it looks worse on benchmarks than Qwen or GLM. I don’t see how sharing the weights hurts anyone? Even when Llama 4 came out and it was a dumpster fire, it didn’t affect me personally.
reply> That only means their training regime is inferior if their predecessors did so much more with so much less
Hard to imagine how that wouldn’t be the case. They probably missed the boat on distilling Claude (or their lawyers said no), they probably didn’t hire an army of math PhDs to write reasoning traces, they don’t have millions of DAUs in a coding agent to train from, and they probably have less money, less experience, fewer top tier researchers, and fewer resources for experiments. They are an underdog without a doubt.
None of that means they shouldn’t release their model.
It depends! If a startup is entering with a large model to face other larger models, it must be better at least in 1 meaningful dimension.
reply500B params performing worse than other OSS of the same size is pretty meaningless if no one will use it.