SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation
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. Recent work augments this paradigm with reasoning traces and optimizes them via reinforcement learning with verifiable rewards, typically outcome-reward algorithm with exact-match feedback on the generated SID. However, in large-catalog recommendation, exact-match feedback on the generated SID only reports whether the final item is correct; when a generated SID mismatches, outcome-reward cannot identify which ...
| Authors | Zaiyi Zheng, Liang Wu, Guanghui Min et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 May 2026 |
| Last updated at source | 15 August 2026 |
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