New Pre-training Algorithm Uses Inverse Gradients to Boost Post-trainability
ivan_bezdomny · x · 2026-07-31
Discusses how open-source models vary in their "post-trainability" during fine-tuning, highlighting a paper suggesting that models on shallower loss curves are easier to post-train.
To enhance this, the paper introduces a novel pre-training algorithm. It works by first finding the worst policy locally via inverse gradients, computing the gradient at that worst policy, and then applying it back to the current policy. This adversarial-like approach aims to make models more adaptable for downstream post-training.
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