Learning User Simulators with Turing Rewards
Learning to simulate human users in interactive settings could advance the training of agent assistants, evaluation of personalization systems, research...
Summary
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 {T
Why it matters
This is part of the steady stream of AI work that reshapes how researchers and builders think about what’s possible. The full details are in the original source below — worth reading directly rather than relying on a brief summary.
Read the original
The primary source has the full paper, announcement, or reporting:
→ https://arxiv.org/abs/2606.19336v1
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