Semiparametric Preference Optimization: Your Language Model is Secretly a Single-Index Model
arXiv:2512.21917v3 Announce Type: replace Abstract: Policy alignment to preference data typically assumes a known link function between observed preferences and latent rewards (e.g., Bradley-Terry model / logistic link). Misspecification of this link can bias inferred rewards and misalign learned policies. We study policy alignment under an unknown and unrestricted link function. We formulate an $f$-divergence-constrained reward maximization problem and show that realizability in a policy class induces a semiparametric single-index binary choice model, where a scalar policy-induced index captu
This research addresses fundamental limitations in policy alignment for AI models, a critical ongoing challenge in developing robust and safe AI systems.
Improving how AI models learn from preferences, even with unknown link functions, directly enhances their alignment with human values and effectiveness in real-world applications.
The ability to achieve better policy alignment under more realistic, unknown link functions means more reliable and adaptable AI, moving beyond prior restrictive assumptions.
- · AI developers
- · Reinforcement learning researchers
- · AI ethicists
- · Users of AI systems
- · Developers relying on rigid preference models
- · Systems with poor alignment
AI models will become more adept at understanding and incorporating nuanced human preferences into their decision-making processes.
This improved alignment could lead to more trustworthy and widely adopted autonomous AI systems across various industries.
Greater societal acceptance and integration of AI may accelerate due to systems that better reflect human intent and values, potentially influencing regulatory frameworks.
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Read at arXiv cs.LG