← Back to all news
arXiv Machine Learning By Siliang Liu, Mohammad Ghasemi, Sapan Patel, Amin Banitalebi-Dehkordi

Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation

Read the original on arXiv Machine Learning →

The paper presents a two-level framework for scalable trade‑up recommendation. Level 1 distills large‑language‑model reasoning into a compact, non‑generative student that classifies product pairs using only precomputed embeddings, achieving high AUC on a benchmark. Level 2 applies product‑type test‑time training to fine‑tune lightweight adapters, further improving performance while keeping inference fast and inexpensive.

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 Machine Learning.

Related stories

arXiv AI
Aug 20

rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation

The paper introduces rEDMRec, a method that compresses a large language model’s reasoning about user preferences and item comparisons into a compact, editable memory. This memory, organized into four channels—long‑term preference, short‑term context, item perception, and counterfactual hard‑negative comparisons—can be updated by an LLM controller and queried by a lightweight student LLM for ranking, eliminating the need to re‑run the expensive teacher model for each request. Experiments on ML‑1M, Amazon Beauty, and Steam datasets show that rEDMRec consistently outperforms zero‑shot, few‑shot, RAG, and GraphRAG baselines, achieving up to a 13.3% improvement in HR@1 on ML‑1M.

By Minh Hoang Nguyen, Tung Le, Huy Tien Nguyen
llmsragagents
More like this →
arXiv AI
Jun 30

Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning

arXiv:2508. 09883v2 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate remarkable reasoning capabilities in tasks such as algorithmic coding and mathematical problem-solving.

By Xiaojun Wu, Xiaoguang Jiang, Huiyang Li, Jucai Zhai, Dengfeng Liu, Qiaobo Hao, Huang Liu, Zhiguo Yang, Ji Xie, Ninglun Gu, Jin Yang, Kailai Zhang, Yelun Bao, Jun Wang
llmsreinforcement-learningfine-tuningefficiencybenchmarks
More like this →
Hugging Face Trending Papers
Aug 13

The Embedder's Dilemma: LLMs Are Better, but at What Cost?

Should you replace your text-embedding pipeline with a large language model? We answer this with a controlled, cost-aware comparison of ten LLMs across six families and 26 embedding models (118M to 14B parameters) on 37 tasks spanning classification, semantic textual similarity (STS), clustering, pair classification, and retrieval.

llmsragbenchmarks
More like this →