Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning (2503.06034v1)

Abstract: In this paper, we introduce Rank-R1, a novel LLM-based reranker that performs reasoning over both the user query and candidate documents before performing the ranking task. Existing document reranking methods based on LLMs typically rely on prompting or fine-tuning LLMs to order or label candidate documents according to their relevance to a query. For Rank-R1, we use a reinforcement learning algorithm along with only a small set of relevance labels (without any reasoning supervision) to enhance the reasoning ability of LLM-based rerankers. Our hypothesis is that adding reasoning capabilities to the rerankers can improve their relevance assessement and ranking capabilities. Our experiments on the TREC DL and BRIGHT datasets show that Rank-R1 is highly effective, especially for complex queries. In particular, we find that Rank-R1 achieves effectiveness on in-domain datasets at par with that of supervised fine-tuning methods, but utilizing only 18\% of the training data used by the fine-tuning methods. We also find that the model largely outperforms zero-shot and supervised fine-tuning when applied to out-of-domain datasets featuring complex queries, especially when a 14B-size model is used. Finally, we qualitatively observe that Rank-R1's reasoning process improves the explainability of the ranking results, opening new opportunities for search engine results presentation and fruition.

Enhancing Document Reranking with Reinforcement Learning: An Examination of Rank-R1

The paper "Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning" introduces a novel approach towards improving document reranking methods in the field of Information Retrieval, particularly focusing on enhancing the reasoning capabilities of LLMs. Historically, document reranking has relied heavily on techniques such as prompting or fine-tuning LLMs to assess and order documents based on their relevance to a specific user query. These traditional methods, while effective, often neglect the underlying reasoning processes that could further enhance performance in understanding complex relevance relationships.

The authors propose Rank-R1, a reranker that distinguishes itself by leveraging reinforcement learning (RL) to boost the reasoning capabilities of LLM-based models. Notably, the model is trained with a significantly reduced set of relevance labels, and without direct reasoning supervision, aiming to cultivate its reasoning abilities intrinsically. The use of RL, specifically Group Relative Policy Optimization (GRPO), as opposed to supervised fine-tuning, stands out in terms of data efficiency and reasoning enhancement. Remarkably, Rank-R1 is shown to perform on par with supervised approaches while using only 18% of the training data typically required for fine-tuning.

Experimental evaluation on both in-domain datasets (TREC DL19 and DL20) and out-of-domain datasets (BRIGHT) reveals the efficacy of Rank-R1, especially for complex queries. The model achieves comparable performance to sophisticated supervised models on in-domain datasets and surpasses both zero-shot and fine-tuned models on out-of-domain datasets. These results highlight the potential for Rank-R1 in cross-domain applications where reasoning over document relevance is significantly intensified.

The methodological framework of Rank-R1 encompasses the adaptation of a Setwise prompting approach aligned with a modified reasoning instruction to foster reasoning before ranking decisions. This integration promotes a robust reasoning stage that precedes the actual relevance decision-making, thereby improving the accuracy and transparency of the ranking process. The resultant reasoning process not only enhances explainability but also introduces new possibilities for the presentation of search results, potentially benefiting applications that require transparent decision-making processes, such as in medical document retrieval.

From a theoretical perspective, the paper posits that combining reinforced reasoning with document relevance estimation yields considerable benefits by mitigating the reliance on extensive annotated data sets and by enhancing transferability across various query domains. Practically, the reduced data requirements and improved explanation capabilities resonate with ongoing efforts to deploy more interpretable AI systems with lesser annotation costs.

Looking forward, the approach outlined in Rank-R1 could inspire future research trajectories focusing on the integration of RL with LLMs to further refine document ranking mechanisms, especially in other areas demanding high interpretability and domain transferability. Additionally, exploring alternative reinforcement strategies or hybrid models incorporating reinforcement learning and self-supervised objectives could pave the way for even greater advancements in this field. The introduction of Rank-R1 invites a reconsideration of current document reranking paradigms, encouraging a broader adoption of reinforced reasoning mechanisms in complex information retrieval tasks.