Distillation and Refinement of Reasoning in Small Language Models for
  Document Re-ranking
Authors: name: Chris Samarinas, name: Hamed Zamani

We present a novel approach for training small language models for
reasoning-intensive document ranking that combines knowledge distillation with
reinforcement learning optimization. While existing methods often rely on
expensive human annotations or large black-box language models, our methodology
leverages web data and a teacher LLM to automatically generate high-quality
training examples with relevance explanations. By framing document ranking as a
reinforcement learning problem and incentivizing explicit reasoning
capabilities, we train a compact 3B parameter language model that achieves
state-of-the-art performance on the BRIGHT benchmark. Our model ranks third on
the leaderboard while using substantially fewer parameters than other
approaches, outperforming models that are over 20 times larger. Through
extensive experiments, we demonstrate that generating explanations during
inference, rather than directly predicting relevance scores, enables more
effective reasoning with smaller language models. The self-supervised nature of
our method offers a scalable and interpretable solution for modern information
retrieval systems.