arXiv:2606.19336v1 Announce Type: new Abstract: Learning to simulate human users in interactive settings could advance the training of agent assistants, evaluation of personalization systems, research in the social sciences, and more. Existing approaches generally do so by training a large language model (LLM) to match a single ground truth response, either by maximizing the log probability or by using a similarity reward. We instead propose {Turing-RL}: a Turing-Test-based reinforcement learning approach for training user simulator models. {Turing-RL} uses a discriminative Turing reward with
The increasing sophistication of LLMs and reinforcement learning techniques makes advanced user simulation a tractable problem, addressing limitations of prior methods.
Improving user simulators will significantly accelerate the development and evaluation of AI agents and personalization systems, crucial for broad AI deployment.
The methodology for training AI models to understand and mimic human behavior in interactive settings becomes more robust and potentially more human-like.
- · AI agent developers
- · Customer service platforms
- · Personalization systems
- · Social science researchers
- · Traditional A/B testing methodologies for AI
- · Less adaptive simulation techniques
More efficient and realistic training of AI assistants and personalized user experiences.
Faster iteration cycles for AI product development due to higher fidelity user simulation.
Potential for user simulators to become indistinguishable from real users in certain contexts, raising ethical and identification questions.
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Read at arXiv cs.CL