Author

Michal Wilinski

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Jul 2026

Inverse RL Helps Align AI by Imitating Humans

It is shown that the recovered reward improves a base policy without a supervised loss and yields further gains when optimized after standard supervised fine-tuning and can be used for contextual alignment, in which a single policy can be tailored to the preferences of different audiences.

Michal Wilinski, Le-Qi Liu, Chirag Nagpal · arXiv.org · 0 citations