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arXiv Machine Learning By Jiawei Chen, Ruoxi Xu, Boxi Cao, Zhuoqun Li, Ruotong Pan, Yunfei Zhang, Zhirui Yang, Jingyan Chen, Tingting Gao, Han Li, Yaojie Lu, Xianpei Han, Le Sun, Xiangyu Wu, Hongyu Lin

OmniSimulator: Aligning Small Language Models for Authentic Heterogeneous Behavior Modeling

Read the original on arXiv Machine Learning →

OmniSimulator is a training framework that enhances small language models for realistic user simulation on short‑video platforms. It structures behavior‑oriented reasoning into five dimensions, selects real user‑behavior data, and uses a teacher model to generate high‑quality reasoning traces, enabling small models to learn behavior‑relevant patterns instead of superficial correlations. The approach yields a 34.33% performance boost for Qwen3‑8B, competitive with Claude‑Sonnet‑4.5, and supports smaller‑scale, self‑hosted deployment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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