OmniSimulator: Aligning Small Language Models for Authentic Heterogeneous Behavior Modeling

Key points

• Large language models (LLMs) have shown great potential as general-purpose user simulators for interactive systems.1
• Prior studies have demonstrated that training on high-quality data can substantially improve model reasoning and downstream task performance.1
• In this paper, we propose OmniSimulator, a novel training framework designed to improve the predictive ability of small LLMs for realistic user simulation on short-video platform environments.1
• These results suggest that learning structured behavior-oriented reasoning patterns is a promising direction towards scalable and high-fidelity LLM-based user simulation.1

Sources (1)

• [1]OmniSimulator: Aligning Small Language Models for Authentic Heterogeneous Behavior Modeling
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 09:30 AM

  Specifically, OmniSimulator organizes behavior-oriented reasoning through a structured schema with five dimensions, carefully selects data from real user-behavior scenarios, and introduces a teacher model to generate high-quality reasoning traces under the proposed schema, helping small models learn behavior-relevant reasoning patterns rather than relying on superficial behavioral correlations.

  Large language models (LLMs) have shown great potential as general-purpose user simulators for interactive systems.

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