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Mitigate the brittleness of RLHF by explicitly controlling for disagreement and tail risk during inference, without retraining, using a KL-robust optimization framework.
Achieve zero-collision embedding tables in production recommenders without sacrificing training speed, unlocking better personalization via fresher and higher-quality item embeddings.
Ditch ANN search altogether: MFLI learns a hierarchical index alongside item embeddings, boosting recall by up to 11.8% and cold-content delivery by 57.29% in large-scale recommender systems.
