Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA
arXiv:2604.23336v3 Announce Type: replace-cross Abstract: Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language models, incurring substantial computational costs. To address this limitation, we propose Rabtriever, which independently encodes queries and documents, while providing comparable cross query-document comprehension capabilities to rerankers. We start from training a LLM-based generative reranker, which puts the document prior to the query and prompts the LLM to generate the relevance score
The increasing computational demands of large language models and complex retrieval systems necessitate more efficient methods to maintain practical scalability and cost-effectiveness.
This development could significantly reduce the computational overhead for AI-powered information retrieval, making advanced search and reasoning more accessible and cost-efficient for a wider range of applications and users.
The proposal of Rabtriever suggests a new paradigm for efficient rationale-based retrieval that can match cross-encoding capabilities with independent query and document encoding, departing from traditional computationally intensive methods.
- · AI developers
- · Cloud computing providers (through increased efficiency)
- · Enterprises adopting advanced retrieval systems
- · Users of information retrieval systems
- · Companies reliant on inefficient, high-cost LLM reranking
- · Legacy search infrastructure that cannot adapt
More sophisticated and context-aware search capabilities become widely deployable due to lower operational costs.
This could accelerate the development of AI agents that rely heavily on efficient and accurate information retrieval for their autonomous functions.
Increased efficiency in information processing might democratize access to advanced AI functionalities, potentially leading to new breakthroughs in various scientific and commercial domains.
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Read at arXiv cs.LG