Groupwise Distortion Guarantees for Preference-Based Alignment
Preference-based alignment methods such as reinforcement learning from human feedback (RLHF) and Nash learning from human feedback (NLHF) aggregate pairwise preferences to learn an LLM policy, but a natural goal is maximizing social welfare (average cardinal utility), which comparisons alone need not identify. Gölz, Haghtalab, and Yang (GHY) measure the gap by distortion: the worst-case ratio between the welfare of the best fixed lottery (distribution over responses) and of the learned lottery. They show NLHF is optimal when every user receives the same lottery. Account-based LLMs, however, have information about their users and can serve different lotteries to different people. We give a...
| Authors | Jacob Brodkey, Roberto Tamez, Aaron Roth |
|---|---|
| Primary category | cs.LG |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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