Adobe's Credit Stabilization through Time makes short-sequence training generalize to long contexts
burkov · x · 2026-10-12
Training recurrent models on extreme sequence lengths is prohibitively expensive and long-context data is scarce, yet short-sequence-trained models fail to generalize. An Adobe article analyzes how learning signals propagate over time and introduces Credit Stabilization through Time, a training intervention aiming to enable cheap short-sequence training with long-horizon deployment.
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