LaRec: Unleashing LLM-based Latent Reasoning for Generative Recommendation
- Published
- Source
- arXiv
- Paper number
- 741
- Field
- Information Retrieval
- arXiv ID
- 2607.24617
Key points
- It greatly reduces response latency by reasoning in latent space instead of through long text reasoning.
- During latent pretraining, step-level alignment and process direction alignment provide rich supervisory signals to intermediate reasoning steps.
- A user-specific Gaussian mixture distribution is used to sample the starting point of reasoning within the relevant latent space.
- GRPO is combined with a sparse-hit reward and a dense semantic-similarity reward to align reasoning with the recommendation objective.
- The latency of latent reasoning is close to no-reasoning and much lower than chain-of-thought.
- An online A/B test on a real advertising platform records improvements such as a 2.93% conversion rate.
Paper links
External research summaries. These are not HDATF publications or measured product results.