Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation

[2608.11980] Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Information Retrieval arXiv:2608.11980 (cs) [Submitted on 12 Aug 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation Authors: Kangning Zhang , Haotian Fang , Xukun Luo , Hao Yin , Yang Gao , Peng Yan , Weiwen Liu , Weinan Zhang , Yong Yu View a PDF of the paper titled Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation, by Kangning Zhang and 8 other authors View PDF HTML (experimental) Abstract: Semantic-ID generative recommenders represent each item as a short sequence of discrete semantic tokens and predict the next item by autoregressively generating this token sequence. This paradigm enables a unified generation interface for item IDs, histories, and item text, but it also creates a structured optimization bottleneck during reward-based post-training: when an early semantic token enters the wrong branch of the item-token space, finite rollout groups rarely reach the ground-truth item, so group-relative optimization receives identical zero rewards and produces no useful advantage. We propose Hint-Conditioned Generative Recommendation (HCGRec), a semantic-ID generative recommendation framework that recovers learning signal for such hard training instances. HCGRec diagnoses each instance with checkpoint rollouts and supplies a minimal target-prefix hint only when the current generator cannot reach the correct item. The model then generates the unhinted suffix under the hinted semantic branch, turning zero-reward groups into informative comparisons over item-token completions. Hinting also changes token identity: hinted prefix tokens are oracle-provided item context, while unhinted suffix tokens are sampled generation actions. We therefore introduce hint-aware credit decomposition, using supervised learning to preserve item-semantic and prefix-structure alignment for hinted tokens and GRPO to optimize the sampled suffix. Experiments on sequential recommendation benchmarks show that HCGRec substantially improves over supervised fine-tuning and vanilla reward-based post-training, while reducing zero-advantage training samples from over 70% to below 20%. The code is accessible at this https URL . Comments: Accepted by CIKM 2026 Subjects: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2608.11980 [cs.IR] (or arXiv:2608.11980v2 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2608.11980 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1145/3799682.3840842 Focus to learn more DOI(s) linking to related resources Submission history From: Kangning Zhang [ view email ] [v1] Wed, 12 Aug 2026 12:13:08 UTC (584 KB) [v2] Sat, 15 Aug 2026 11:55:30 UTC (615 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation, by Kangning Zhang and 8 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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