Click-through rate (CTR) prediction is a critical task for e-commerce advertising systems. However, industrial ranking models still rely on sparse IDs and historical behavior signals, which limits their generalization to cold-start products, long-tail queries, and complex user intents. Although large language models (LLMs) provide strong semantic understanding and can generate behavioral rationales, directly using them for online ranking is impractical under strict latency constraints.

We propose GRAM-V2, a rationale-supervised ranking framework that transfers LLM-generated behavioral rationales into an efficient CTR model. GRAM-V2 uses a Teacher LLM to generate click-oriented rationales, distills them into a lightweight Student LLM, and aligns a two-tower CTR model with the Student’s representations and rationale signals. During online serving, GRAM-V2 uses only distilled representations and cached item embeddings, without generating rationales autoregressively. Large-scale offline experiments and online A/B testing show that GRAM-V2 improves CTR prediction accuracy, CTR, and advertising revenue while satisfying production latency constraints.