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Stars and mentions · Last 30 days, Oct 6 – Nov 4
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Mentions
@Sxandchenxing @micahgoldblum The Marin folks tried Hyperball and still found z-loss crucial in preventing loss from blowing up! marin#8227 @classiclarryd
I was really impressed by Marin's published evals policies: marin#9409 Precise and very clearly written.
@CatherineeLii cool experiment! i was wondering whether you believe this high-data-budget fine-tuning gap will persist across batch sizes + weight decays? many researchers find that Muon's critical batch size is much higher alphaxiv.org marin#1565 cc @leloykun t.co
3/3 Finding the right hinting primitives for agentic benchmarks (longer, messier rollouts) is an open question. There’s already some work happening on this front in Marin! Issue: marin#9541
Of course this is retrospective. Hindsight etc. etc. That said, it was basically "do what we did for the Paloma fit". No 535b losses need have been consulted. As always, code and context are on GitHub marin-logbooks#8
We’re Marin, the details are pretty much always available! marin#9960 This time we’re deploying Triton router top-k and short conv, leaner ragged a2a transport, fused expert backward, and XLA scheduling fixes