# Weighted KL-Divergence for Document Ranking Model Refinement

arXiv: https://arxiv.org/abs/2406.05977 · HTML: https://mlsanity.com/paper/2406.05977/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [Through Their Eyes: Fixation-aligned Tuning for Personalized User Emulation](https://mlsanity.com/paper/2604.09368/) (2026)

## Abstract (arXiv)

Transformer-based retrieval and reranking models for text document search are often refined through knowledge distillation together with contrastive learning. A tight distribution matching between the teacher and student models can be hard as over-calibration may degrade training effectiveness when a teacher does not perform well. This paper contrastively reweights KL divergence terms to prioritize the alignment between a student and a teacher model for proper separation of positive and negative documents. This paper analyzes and evaluates the proposed loss function on the MS MARCO and BEIR datasets to demonstrate its effectiveness in improving the relevance of tested student models.