Stanford PhD student open-sources impersona-env: RL training for personal assistants via simulated users
kenziyuliu · x · 2026-10-06
Ben Shi announced he has started a CS PhD at Stanford and released a proof-of-concept project, impersona-env, for training personalized assistants by building an RL environment out of user models.
Key ideas:
- The assistant learns by repeatedly probing a simulated version of the user—asking questions, surfacing latent thinking and feedback, and updating its priors accordingly.
- The author argues this unlocks personalization beyond discrete fact recall: his personal assistant model already understands nuances of his preferences he didn't know about himself.
- Open problems: building user-model evals that generalize across users and downstream applications, and representing longitudinal user information.
Still a proof of concept, with a full blog post available.
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