arXiv:2605.26554v1 Announce Type: new Abstract: Contextual dueling bandits form a cornerstone of preference-based decision-making, with critical applications in recommender systems and large language model alignment. However, standard algorithms rely on the idealized assumption of immediate feedback, a condition frequently violated in real-world scenarios such as prompt optimization. This setting introduces a unique theoretical challenge: unlike linear bandits, dueling bandit estimators lack closed-form solutions, rendering naive adaptations of standard weighting techniques biased. To address
The increasing complexity and real-world deployment of AI systems, particularly large language models and recommender systems, are highlighting the limitations of idealized feedback mechanisms.
Improving the efficiency and reliability of preference-based decision-making under delayed feedback is crucial for robust AI alignment and optimized user experiences in critical applications.
This research introduces methodologies to overcome theoretical challenges in dueling bandits with delayed feedback, previously hindering their practical application in dynamic, real-world AI systems.
- · AI developers
- · Recommender system providers
- · Large language model developers
- · Users of AI-powered systems
- · AI systems with poor alignment
- · Inefficient recommendation engines
More effective and adaptable AI systems, particularly in areas requiring continuous learning from human preferences.
Accelerated development and adoption of AI assistants and automated decision-making tools that learn from real-time, imperfect user interaction.
Enhanced overall trust and utility of AI across diverse applications due to improved understanding and response to human preferences.
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Read at arXiv cs.LG