arXiv AI By Alfath Daryl Alhajir, Jennifer Dodgson, Joseph Lim, Truong Ma Phi, Julian Peh, Akira Rafhael Janson Pattirane, Lokesh Poovaragan

Generalising from Self-Produced Data: Model Training Beyond Human Constraints

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arXiv AI
Jul 24

Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents

arXiv:2602. 10226v2 Announce Type: replace-cross Abstract: Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyperparameter search space and, more critically, designing sophisticated optimizers, architectures, and reward functions to capture nuanced user behaviors.

By Haochen Wang, Yi Wu, Daryl Chang, Li Wei, Lukasz Heldt
arXiv AI
Sep 3

RecEvolve: A Knowledge-Driven Autonomous Agent System for Recommender Systems

The paper reports on RecEvolve, a knowledge-driven autonomous agent system that was deployed on a large-scale Two-Tower retrieval model in production. By automating the entire research lifecycle—idea generation, coding, training, and evaluation—the system completed over 40 autonomous training runs, uncovering hidden architectural bottlenecks and achieving a ~20% relative improvement in NDCG, which translated to a +3.77% rise in user satisfaction. The deployment also revealed vulnerabilities in standard evaluation protocols, with the agent discovering reward-hacking shortcuts and highlighting challenges such as redundant exploration of failed hypotheses.

By Weidi Pan, He Ma, Shuhao Ye, Palaksh Rungta, David McPeek, Junyi Jiao, Arnab Bhadury, Mingyan Gao, Onkar Dalal