通过分步验证提升生成推荐的准确性，让小模型也能逼近大模型效果。
PROMISE: Process Reward Models Unlock Test-Time Scaling Laws in Generative Recommendations
- 引入轻量级过程奖励模型，逐步评估生成中间结果质量。
- 增加推理计算量可显著提升小模型性能，突破传统规模限制。
- 适合追求高效部署且重视推荐精准度的工业级推荐系统。
生成式推荐作为一种新兴范式，将推荐任务重新定义为对层级语义ID的序列到序列生成。然而，现有方法存在我们称之为语义漂移的问题：早期高层级标记的错误会不可逆地引导生成轨迹进入无关语义空间。受用于增强大语言模型推理能力的过程奖励模型（PRM）启发，我们提出Promise框架，将密集的、分步的验证机制融入生成模型。该框架包含一个轻量级PRM，用于评估中间推理步骤的质量，并结合PRM引导的束搜索策略，利用密集反馈动态剪枝错误分支。关键在于，我们的方法解锁了推荐系统的测试时扩展定律：通过增加推理计算，小模型可达到甚至超越大模型性能。大规模离线实验与在线A/B测试结果表明，Promise能有效缓解语义漂移，显著提升推荐准确率，同时支持高效部署。
原文摘要 · Abstract (English)
Generative Recommendation has emerged as a promising paradigm, reformulating recommendation as a sequence-to-sequence generation task over hierarchical Semantic IDs. However, existing methods suffer from a critical issue we term Semantic Drift, where errors in early, high-level tokens irreversibly divert the generation trajectory into irrelevant semantic subspaces. Inspired by Process Reward Models (PRMs) that enhance reasoning in Large Language Models, we propose Promise, a novel framework that integrates dense, step-by-step verification into generative models. Promise features a lightweight PRM to assess the quality of intermediate inference steps, coupled with a PRM-guided Beam Search strategy that leverages dense feedback to dynamically prune erroneous branches. Crucially, our approach unlocks Test-Time Scaling Laws for recommender systems: by increasing inference compute, smaller models can match or surpass larger models. Extensive offline experiments and online A/B tests on a large-scale platform demonstrate that Promise effectively mitigates Semantic Drift, significantly improving recommendation accuracy while enabling efficient deployment.
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