Implicit Reasoning for Large Language Model-based Generative Recommendation
Quick Answer
PauseRec introduces a lightweight implicit reasoning framework for LLM-based Generative Recommendation, outperforming explicit CoT methods by 6.22%, reducing training costs by 65% GPU hours, and accelerating inference by 71.3%.
Quick Take
PauseRec introduces a lightweight implicit reasoning framework for LLM-based Generative Recommendation, outperforming explicit CoT methods by 6.22%, reducing training costs by 65% GPU hours, and accelerating inference by 71.3%. This approach addresses limitations in existing reasoning pipelines, enhancing efficiency and effectiveness in leveraging pretrained knowledge.
Key Points
- PauseRec outperforms explicit reasoning methods by 6.22% in performance.
- Training costs are reduced by up to 65% in GPU hours.
- Inference speed is accelerated by up to 71.3% with PauseRec.
- Existing pipelines struggle with world-knowledge verbalization and SID alignment.
- PauseRec avoids costly reasoning trace acquisition and alignment training.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 14142v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge. Yet reliably invoking this knowledge for GR remains poorly understood. A key obstacle is that LLM-based GR typically represents items with Semantic IDs (SIDs), disrupting LLMs' natural-language reasoning interface because these tokens are unseen by the LLM during pretraining.
Existing approaches address this with expensive multi-stage pipelines that ground SIDs and elicit explicit rationales, but offer limited insight into when and why each stage is necessary. In this work, we systematically decompose explicit reasoning training pipelines for LLM-based GR, revealing three key limitations: weakened world-knowledge verbalization, misalignment between SID and natural-language token embedding spaces, and sensitivity to rationale quality, all of which hurt explicit reasoning performance.
To circumvent these issues, we propose PauseRec, a lightweight implicit reasoning paradigm tailored for GR. PauseRec is exceptionally practical, avoiding costly reasoning trace acquisition and reasoning alignment training, leading to a multitude of benefits: (1) it outperforms standard explicit CoT methods by up to 6. 22%, (2) it reduces training cost by up to 65% GPU hours, and (3) it speeds up inference by up to 71. 3%.
These results position PauseRec as a lightweight alternative to explicit rationale generation, enabling more effective and efficient LLM-based GR.
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