Semiparametric Preference Optimization: Your Language Model is Secretly a Single-Index Model

1. 1Formulate an $f$-divergence-constrained reward maximization problem
2. 2Show realizability in a policy class
3. 3Apply semiparametric preference optimization to policy alignment
4. 4Evaluate the performance of the optimized policy
5. 5Analyze the impact of link function misspecification on policy alignment

AI engineers and researchers benefit from this approach as it improves policy alignment and reward inference, while data scientists can apply this to real-world problems

💡 Semiparametric preference optimization can handle unknown and unrestricted link functions, reducing bias in inferred rewards and improving policy alignment

🤖 Optimize policy alignment with unknown link functions using semiparametric preference optimization! 🚀