Human-AI Curation Synergy: Scaling Preference Data Curation via Human-Guided AI Feedback

ICLR 2026 Conference Submission13447 Authors

Keywords: preference data, reward modeling, data curation, data annotation

TL;DR: Through a human-AI synergistic curation pipeline, we curate a high-quality, large-scale preference data mixture of 40 million preference pairs, enabling state-of-the-art reward models on seven major reward model benchmarks.

Abstract: Despite the critical role of reward models (RMs) in reinforcement learning from human feedback (RLHF), current state-of-the-art open RMs perform poorly on most existing evaluation benchmarks, failing to capture the spectrum of nuanced and sophisticated human preferences. Even approaches incorporating advanced training techniques have failed to yield meaningful performance improvements. We hypothesize that this brittleness stems primarily from limitations in preference datasets, which are often narrowly scoped, synthetically labeled, or lack rigorous quality control. To address these challenges, we present a large-scale preference dataset comprising 40 million preference pairs. To enable data curation at scale, we design a human-AI synergistic two-stage pipeline that leverages the complementary strengths of human annotation quality and AI scalability. In this pipeline, humans provide verified annotations, while large language models~(LLMs) perform automatic curation based on human guidance. Based on this preference mixture, we train simple Bradley-Terry reward models ranging from 0.6B to 8B parameters on a carefully curated subset of 26 million preference pairs from the 40M pool. We demonstrate that the resulting reward models are versatile across a wide range of capabilities, including alignment with human preferences, objective correctness, safety, resistance to stylistic biases, and best-of-N scaling. These reward models achieve state-of-the-art performance across seven major reward model benchmarks, outperform the latest paradigm of generative reward models, and demonstrate strong downstream performance. Ablation studies confirm that the effectiveness of our approach stems not only from data scale but also from high-quality curation. Our approach represents substantial progress in open reward models, revealing the untapped potential of existing preference datasets and demonstrating how human-AI curation synergy can unlock significantly higher data quality.

Primary Area: datasets and benchmarks

Submission Number: 13447