REIGN Enables Efficient Context-Length Scaling for Retrieval
_reachsumit · x · 2026-09-01
Addressing the high cost of dense retrieval over long documents, the paper proposes REIGN, a bi-encoder operating on cached chunk embeddings.
Method:
- Uses a frozen Guidance Network (GN) to encode documents into chunk embeddings.
- Trains a lightweight encoder on top of these cached embeddings, decoupling token-level processing from document-level reasoning.
Advantages:
- Cuts per-document training cost by 4 orders of magnitude vs chunked Transformer fine-tuning.
- Matches dense long-context retrievers with smaller parameter budgets.
Datasets: Releases a synthetic long-document retrieval benchmark.
Results: Matches or rivals larger models (1.6x-4.3x params) on Wikipedia, LoCo, and patent retrieval tasks.
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