# Which Tricks Are Important for Learning to Rank?

arXiv: https://arxiv.org/abs/2204.01500 · HTML: https://mlsanity.com/paper/2204.01500/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Introducing LETOR 4.0 Datasets](https://mlsanity.com/paper/1306.2597/) (2013)
- [StochasticRank: Global Optimization of Scale-Free Discrete Functions](https://mlsanity.com/paper/2003.02122/) (2020)
- [SGLB: Stochastic Gradient Langevin Boosting](https://mlsanity.com/paper/2001.07248/) (2020)

## Built on by (most cited first, 3 of 3)

- [On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation](https://mlsanity.com/paper/2307.15053/) (2023)
- [Gradient Boosting Reinforcement Learning](https://mlsanity.com/paper/2407.08250/) (2024)
- [RMB: Reward Model Boosting Mitigates Reward Hacking](https://mlsanity.com/paper/2609.33221/) (2026)

## Abstract (arXiv)

Nowadays, state-of-the-art learning-to-rank methods are based on gradient-boosted decision trees (GBDT). The most well-known algorithm is LambdaMART which was proposed more than a decade ago. Recently, several other GBDT-based ranking algorithms were proposed. In this paper, we thoroughly analyze these methods in a unified setup. In particular, we address the following questions. Is direct optimization of a smoothed ranking loss preferable over optimizing a convex surrogate? How to properly construct and smooth surrogate ranking losses? To address these questions, we compare LambdaMART with YetiRank and StochasticRank methods and their modifications. We also propose a simple improvement of the YetiRank approach that allows for optimizing specific ranking loss functions. As a result, we gain insights into learning-to-rank techniques and obtain a new state-of-the-art algorithm.