---
type: source
title: "PAL: Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment"
author: "Ramya Lab (ICLR 2025)"
url: https://pal-alignment.github.io/
date: 2025-01-21
domain: ai-alignment
secondary_domains: [collective-intelligence]
format: paper
status: unprocessed
priority: high
tags: [pluralistic-alignment, reward-modeling, mixture-models, ideal-points, personalization, sample-efficiency]
---

## Content

PAL is a reward modeling framework for pluralistic alignment that uses mixture modeling inspired by the ideal point model (Coombs 1950). Rather than assuming homogeneous preferences, it models user preferences as a convex combination of K prototypical ideal points.

**Architecture:**
- Model A: K prototypical ideal points representing shared subgroup structures
- Model B: K prototypical functions mapping input prompts to ideal points
- Each user's individuality captured through learned weights over shared prototypes
- Distance-based comparisons in embedding space

**Key Results:**
- Reddit TL;DR: 1.7% higher accuracy on seen users, 36% higher on unseen users vs. P-DPO, with 100× fewer parameters
- Pick-a-Pic v2: Matches PickScore with 165× fewer parameters
- Synthetic: 100% accuracy as K approaches true K*, vs. 75.4% for homogeneous models
- 20 samples sufficient per unseen user for performance parity

**Formal Properties:**
- Theorem 1: Per-user sample complexity of Õ(K) vs. Õ(D) for non-mixture approaches
- Theorem 2: Few-shot generalization bounds scale with K not input dimensionality
- Complementary to existing RLHF/DPO pipelines

**Venues:** ICLR 2025 (main), NeurIPS 2024 workshops (AFM, Behavioral ML, FITML, Pluralistic-Alignment, SoLaR)

Open source: github.com/RamyaLab/pluralistic-alignment

## Agent Notes
**Why this matters:** This is the first pluralistic alignment mechanism with formal sample-efficiency guarantees. It demonstrates that handling diverse preferences doesn't require proportionally more data — the mixture structure enables amortization.
**What surprised me:** The 36% improvement for unseen users. Pluralistic approaches don't just handle existing diversity better — they generalize to NEW users better. This is a strong argument that diversity is not just fair but functionally superior.
**What I expected but didn't find:** No comparison with RLCF/bridging approaches. No analysis of whether the K prototypes correspond to meaningful demographic or value groups.
**KB connections:** Directly addresses [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]] by providing a constructive alternative. Connects to [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]].
**Extraction hints:** Extract claims about: (1) mixture modeling enabling sample-efficient pluralistic alignment, (2) pluralistic approaches outperforming homogeneous ones for unseen users, (3) formal sample complexity bounds for personalized alignment.
**Context:** Part of the growing pluralistic alignment subfield. Published by Ramya Lab, accepted at top venue ICLR 2025.

## Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values
WHY ARCHIVED: First mechanism with formal guarantees for pluralistic alignment — transitions the KB from impossibility diagnosis to constructive alternatives
EXTRACTION HINT: Focus on the formal properties (Theorems 1 and 2) and the functional superiority claim (diverse approaches generalize better, not just fairer)
