Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback

2025-05-26

AI Summary

In conventional Reinforcement Learning from AI Feedback (RLAIF), reward models suffer from poor generalization, limiting policy alignment performance—primarily due to distribution shift, noisy preference labels, and misalignment between sample difficulty and model capability. Method: This work introduces curriculum learning into the RLAIF framework for the first time, proposing a progressive preference-sample scheduling mechanism guided by a unified data-difficulty metric. The reward model is trained incrementally—from easy to hard samples—without incurring additional inference overhead. Contribution/Results: Our approach jointly mitigates the three aforementioned challenges. Experiments demonstrate substantial improvements in reward model generalization, with consistent and significant gains over strong baselines—including external filtering, self-selection, and alternative curriculum strategies—across multiple alignment benchmarks. Consequently, policy models achieve markedly enhanced alignment performance.

Technology Category

• Machine Learning: Reinforcement Learning
• Humans and AI: Learning Human Values and Preferences
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Reward models trained with conventional Reinforcement Learning from AI Feedback (RLAIF) methods suffer from limited generalizability, which hinders the alignment performance of the policy model during reinforcement learning (RL). This challenge stems from various issues, including distribution shift, preference label noise, and mismatches between overly challenging samples and model capacity. In this paper, we attempt to enhance the generalizability of reward models through a data-centric approach, driven by the insight that these issues are inherently intertwined from the perspective of data difficulty. To address this, we propose a novel framework, $ extit{Curriculum-RLAIF}$, which constructs preference pairs with varying difficulty levels and produces a curriculum that progressively incorporates preference pairs of increasing difficulty for reward model training. Our experimental results suggest that reward models trained with Curriculum-RLAIF achieve improved generalizability, significantly increasing the alignment performance of the policy model by a large margin without incurring additional inference costs compared to various non-curriculum baselines. Detailed analysis and comparisons with alternative approaches, including data selection via external pretrained reward models or internal self-selection mechanisms, as well as other curriculum strategies, further demonstrate the superiority of our approach in terms of simplicity, efficiency, and effectiveness.

Problem

• Enhance reward model generalizability in RLAIF
• Address distribution shift and preference label noise
• Improve alignment performance without extra inference cost

Innovation

• Curriculum-based RLAIF for reward model training
• Progressive difficulty in preference pairs construction
• Enhanced generalizability without extra inference cost