Summary of Pareto-optimal Learning From Preferences with Hidden Context, by Ryan Bahlous-boldi et al.
Pareto-Optimal Learning from Preferences with Hidden Context
by Ryan Bahlous-Boldi, Li Ding, Lee Spector, Scott Niekum
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Pareto Optimal Preference Learning (POPL) framework enables pluralistic alignment by framing discrepant group preferences as objectives with potential trade-offs, aiming to learn policies that are Pareto-optimal on the preference dataset. This is achieved through lexicase selection, an iterative process that selects diverse and Pareto-optimal solutions. POPL surpasses baseline methods in learning sets of reward functions and policies, effectively catering to distinct groups without access to group numbers or membership labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary POPL helps AI models align with human values by learning from diverse populations. This is important for safety and functionality. The framework uses lexicase selection to find the best solutions that satisfy different preferences. POPL outperforms other methods in learning reward functions and policies, making it fair to all groups. |
Keywords
* Artificial intelligence * Alignment
