SAIL: SAILING BEYOND TEXT VIA BEHAVIOR- GROUNDED LLM SUMMARIES

Ads recommendation systems must transfer signals from the organic content side to the ads side, but content items and ads sit in separate modeling spaces with no directly alignable ID representation and text that rarely shares words, so rich behavioral signals on the organic side are hard to reuse commercially. Existing approaches use either discrete Semantic IDs or text-derived representations such as pretrained embeddings and prompt-generated summaries. In our evaluated cross-domain setting, raw-text baselines and prompt-only LLM summaries exhibit a similar empirical surface-representation ceiling where performance saturates around a shared reference level. We propose Sail, a two-stage post-training recipe that keeps the LLM's contribution in explicit natural language rather than opaque Semantic IDs: SFT distills a structured summary from a supply-grounded teacher, denoising raw text and initializing the schema with weak behavioral context; RL with a listwise retrieval reward then performs retrieval alignment, selecting and shaping the collaborative signals that make the summary useful for matching items to ads. Co-occurrence context is used only as training-time privileged information; at inference the summary is written from item text alone. On I2A-Bench, Sail raises AUC by $+2.53$ points over a strong raw-text baseline, remains effective under an independent judge and transfers to a different evaluation protocol, and its largest performance drops occur when fields capturing commercial and audience-bridging signals are removed. The learned summary also transfers, without retraining, to a downstream LLM-based CTR task and improves macro-average AUC over seven objectives.