Merge and Branch: Efficient Tree-Structured Supervised Fine-Tuning of LLMs for Recommendation

Merge and Branch (M&B) is a tree‑structured supervised fine‑tuning method for large language models (LLMs) in recommendation systems. It compresses each historical item’s semantic identifier (SID) and text into a few learned tokens (Merge) and then uses the compressed history to predict multiple target SIDs (Branch), organizing each example as a shallow, wide tree. The approach includes a tree FlashAttention mechanism for efficient training and a tree‑structured key/value cache for constrained decoding, achieving better recommendation performance and improved training and decoding efficiency compared to strong baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• fine-tuning
• benchmarks