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Behavioral foundation models need smaller embedders, study finds

By PulseAugur Editorial · [2 sources] ·

Researchers have published a study on scaling laws for behavioral foundation models, which are trained on user event sequences for applications like recommendation and fraud detection. The study found that a smaller embedder, comprising about 2% of parameters, is compute-optimal across various training budgets. The optimal training strategy shifts towards being more data-heavy at lower compute levels and approaches the Chinchilla heuristic as compute increases. AI

IMPACT Identifies optimal parameter splits and training strategies for behavioral foundation models, potentially guiding future development and resource allocation.

RANK_REASON The cluster contains a research paper detailing findings on scaling laws for behavioral foundation models.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Behavioral foundation models need smaller embedders, study finds

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Rickard Br\"uel Gabrielsson ·

    Scaling Laws for Behavioral Foundation Models over User Event Sequences

    arXiv:2606.05257v1 Announce Type: new Abstract: Foundation models are increasingly trained on sequences of user actions in recommendation, payments, fraud, and commerce, but these models still lack the kind of compute calibration that scaling laws provide for language models. We …

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Rickard Brüel Gabrielsson ·

    Scaling Laws for Behavioral Foundation Models over User Event Sequences

    Foundation models are increasingly trained on sequences of user actions in recommendation, payments, fraud, and commerce, but these models still lack the kind of compute calibration that scaling laws provide for language models. We study a common two-part behavioral-model archite…

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