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Now the masked_token_weighted is learning. We ablated the inpainting task, swapped MSE for SmoothL1Loss (more robust to outliers), and per-dim normalized the reconstruction targets, significantly reducing curvature-dim dominance. ref: https://t.co/FL5X61xpbQ https://t.co/0j03IXFXR2
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"text": "Now the masked_token_weighted is learning. We ablated the inpainting task, swapped MSE for SmoothL1Loss (more robust to outliers), and per-dim normalized the reconstruction targets, significantly reducing curvature-dim dominance. \n\nref: https://t.co/FL5X61xpbQ https://t.co/0j03IXFXR2",
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"text": "Training a 10M params foundation model on 8xH100s.\n\nThe regime is self-supervised pretraining on 29GB of CAD and engineering meshes with masked token modeling, contrastive consistency, and spatial inpainting. \nYou could guess what it is for. https://t.co/y9c9v5d8fN",
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