Author

Chirag Nagpal

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#artificial intelligence Preprint Sep 2026

MaD-RL: Matching Distributions for Calibrating LLMs with Reinforcement Learning

This work proposes a general RL-based framework for Distribution Matching allowing matching the distribution of a latent categorical attribute of model outputs to a specified target distribution and proposes reward functions for other divergences such as KL and Jensen-Shannon and motivate them with theoretical justific...

Sourabh Kulkarni, Ksheeraj Sai Vepuri, B. Demir et al. · 0 citations

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