SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation
The article discusses a new approach called SAPO, which stands for Step-Aligned Policy Optimization, aimed at improving generative recommendation systems. SAPO enhances the process of next-item prediction by optimizing reasoning steps through reinforcement learning. The method shows significant improvements in recommendation accuracy, particularly in scenarios with sparse feedback.
- ▪SAPO optimizes reasoning steps in generative recommendation systems using reinforcement learning.
- ▪The approach computes separate advantages for each reasoning step instead of applying a single advantage to the entire response.
- ▪SAPO has been tested on three real-world recommendation datasets, showing consistent improvements over existing methods.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17648 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | kp-eCShy9aLA |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.17648 (cs) [Submitted on 17 May 2026] Title:SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation Authors:Zaiyi Zheng, Guanghui Min, Yaochen Zhu, Liang Wu, Liangjie Hong, Chen Chen, Jundong Li View a PDF of the paper titled SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation, by Zaiyi Zheng and 6 other authors View PDF HTML (experimental) Abstract:Generative recommendation treats next-item prediction as autoregressive item-identifier generation. Specifically, items are encoded as semantic identifiers (SIDs), which are short coarse-to-fine token sequences whose early tokens capture broad semantics and later tokens refine them.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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