Vikhyat K. highlights how GLM-5.2 reached its final form through an unusually quick post-training step that first spins up multiple expert variants in parallel before folding them into one base model, which the process uses to concentrate learning signals more effectively than standard approaches.
@teortaxesTex @didier_lopes this is after training all of the expert variants and collapse them back into the same base model? seems plausible to me the signal is much more dense in that phase
@didier_lopes > The entire OPD post-training of GLM-5.2 took on this slime platform took ~2 days.
what
The approach trains separate experts then collapses them back together, a step observers see as viable for sharpening the overall training signal within the two-day window reported for GLM-5.2.
No independent verification of the exact mechanics or broader performance lift has surfaced yet, leaving open how widely the slime framework method might transfer beyond this release.
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@vikhyatk @didier_lopes yeah the word "entire" was throwing me off but for OPD I guess makes sense, if they encounter no problems
@teortaxesTex @didier_lopes this is after training all of the expert variants and collapse them back into the same base model? seems plausible to me the signal is much more dense in that phase