arXiv:2605.26385v1 Announce Type: cross Abstract: Large-scale search, recommendation, and retrieval-augmented generation (RAG) systems typically employ a two-stage architecture: an early-stage ranker (ESR) generates a candidate set, which is subsequently re-ranked by a late-stage ranker (LSR). While there are many reinforcement learning (RL) methods for training the LSR, end-to-end training of the ESR has proven challenging. In particular, naive application of "vanilla" policy gradient (V-PG) is not scalable for candidate-set sizes relevant for practical use due to exploding variance. This iss
The increasing complexity and scale of AI systems, particularly in retrieval-augmented generation, are driving the need for more efficient and robust training methods for early-stage search components.
Improving early-stage retrieval is critical for the performance and scalability of large-scale AI applications, directly impacting user experience and the computational efficiency of these systems.
This paper presents a new optimization method that promises to make end-to-end training of early-stage rankers more feasible and scalable for practical applications, overcoming previous limitations of high variance.
- · AI/ML researchers
- · Large-scale search platforms
- · RAG system developers
- · E-commerce platforms
- · Legacy search optimization methods
- · Systems with inefficient early-stage retrieval
More accurate and efficient information retrieval in large-scale AI systems.
Improved user satisfaction and faster development cycles for complex AI applications like RAG.
Potential for new AI services and products that rely on highly efficient and nuanced information access.
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Read at arXiv cs.AI