PROMISE: Process Reward Models for Unlocking Test-Time Scaling Laws in Generative Recommendations

Abstract

Generative Recommendation has emerged as a promising paradigm, reformulating recommendation as a sequence-to-sequence generation task over hierarchical Semantic IDs. However, current approaches face a severe challenge that we define as 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. Our framework utilizes a lightweight PRM to assess the quality of intermediate inference steps, and a PRM-guided beam search strategy that leverages dense feedback to dynamically prune erroneous branches. Most importantly, this method unlocks Test-Time Scaling Laws in recommender systems, demonstrating that 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.