arXiv: cs.AI preprintsInternational6 November 2026
Merge and Branch: Efficient Tree-Structured Supervised Fine-Tuning of LLMs for Recommendation
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arXiv:2610.37973v2 Announce Type: replace-cross Abstract: LLM-based generative recommendation reads long, text-rich interaction histories but generates only short semantic identifiers (SIDs). This asymmetry makes supervised fine-tuning inefficient: encoding the history is expensive, yet each history usually provides supervision for only one target. We present Merge and Branch (M&B) to reduce input cost and learn more from each encoded history. Merge compresses each historical item's SID and text into a small number of learned tokens. Branch uses the compressed history to predict multiple targe
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