We cut a 321B coding model in half to run it on a home PC. Here is what broke, and how we fixed it.
GLM-5.3-Flash has 288 experts in every layer. We kept 152, chosen by optimization, and published the result as open weights. Along the way our first release broke arithmetic, and the smaller files fell into a reasoning loop that one setting removes.
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A 4B model beat a 70B one, and a 284B commercial API, at explaining financial data. Here is how we measured it.
We fine-tuned eleven open-weight models, from 4B to 70B parameters, to explain seasonal market statistics. The smallest one won, and a commercial API lost on every behavioural axis we measured.
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+23 points on a 70B open model, without new human data. What RLVR does, and what it doesn't.
We raised Apertus-70B from 41.6% to 64.8% on held-out coding problems in one round, by letting the model learn only from its own answers that passed the tests. We also found where the method stops.
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This research used compute from EuroHPC Joint Undertaking allocations on the LUMI supercomputer.