Revela: Dense Retriever Learning via Language Modeling

ICLROral2026

Authors
Fengyu Cai, Tong Chen, Xinran Zhao, Sihao Chen, Hongming Zhang, Tongshuang Wu, Iryna Gurevych, Heinz Koeppl
Affiliation
Technische Universität Darmstadt
Venue
ICLR 2026
Track
Oral

TL;DR

Dense retrievers play a vital role in accessing external and specialized knowledge to augment language models (LMs). Training dense retrievers typically requires annotated query-document pairs, which are costly to create and scarce in specialized domains (e.g., code) or in complex settings (e.g., requiring reasoning).

Opening excerpt from the authors’ abstract. source

Read the paper

Topics

language model reasoning

← All ICLR 2026 Oral papers · Browse the whole archive