Naver Webtoon Proposes Three-Phase Alignment Framework for Recommender Foundation Models
_reachsumit · x · 2026-08-10
Naver Webtoon proposed a three-phase progressive post-training framework for recommender foundation models to solve the misalignment between task-specific optimization and practical business metrics.
The framework explicitly separates downstream adaptation from business-metric alignment: Linear Probing stabilizes downstream heads, Full Fine-Tuning specializes the model for target tasks, and Reinforcement Fine-Tuning (RFT) aligns the model with actual business objectives using a learned reward model. Experiments show this progressive framework outperforms single-phase alternatives.
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