arXiv:2606.08480v1 Announce Type: new Abstract: Reinforcement learning (RL) presents a promising avenue for enhancing generative recommendation beyond supervised imitation, leveraging reward signals to guide policy improvement. However, its efficacy is critically contingent on the trustworthiness of the reward model for the samples it evaluates. In practice, production rankers, the widely adopted reward models, are trained on exposure-biased logs, leading to sample-dependent inaccuracies that violate this assumption. Our stratified analysis uncovers a consistent pattern: reward guidance is mos
The increasing sophistication and widespread adoption of generative AI systems for recommendation fuel the need for robust and reliable learning methods that can overcome inherent data biases.
Improving how AI systems learn from imperfect, real-world data directly impacts the efficacy and fairness of AI-driven recommendations across e-commerce, content, and beyond, driving better user experience and potentially higher conversion rates.
This research introduces a method to make reinforcement learning in generative recommendation more resilient to noisy or biased reward signals, leading to more trustworthy and effective AI-powered suggestions.
- · AI researchers and data scientists
- · E-commerce platforms
- · Content recommendation services
- · Users benefiting from better recommendations
- · Companies relying on naive RL implementations
- · Reward models insensitive to noise
More accurate and user-centric personalized recommendations will become prevalent.
This could lead to increased user engagement and revenue for platforms that effectively implement these advanced RL techniques.
Improved recommendation systems might further consolidate market power among platforms with superior AI capabilities, impacting smaller players.
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Read at arXiv cs.LG