SmallReason-ColBERT: An Ultra-Small Late-Interaction Retriever for Reasoning Intensive Retrieval

Abstract

Reasoning-intensive retrieval remains difficult for small models. Compact public ColBERTs are usually trained on general-purpose corpora and underperform reasoning-tuned 150M+ baselines on BRIGHT~\cite{bright} by several nDCG@10 points. However, no public reasoning-tuned ColBERT exists at edge scale. We introduce \textbf{SmallReason-ColBERT}, a 32M late-interaction retriever that closes much of this gap with three components: a varied-length contrastive warmup on ReasonIR-VL, a hard-negative contrastive polish on merged ReasonIR-HQ and BGE-Reasoner data, and a single-layer per-query-token importance head trained on top of the frozen base. The head is trained with an un-normalised weighted MaxSim score and evaluated with its length-normalised form. In a controlled re-training, replacing this training objective with the symmetric normalised score causes the loss to stall and costs $3.59$ nDCG@10. The full recipe reaches \textbf{21.41} mean nDCG@10 on BRIGHT, within $1.21$ of the 150M Reason-ModernColBERT (22.62) and above all $\le 33$M ColBERTs we evaluate. Through ablations on capacity, initialisation, and score variants, we further show that the learned head outperforms fixed IDF weighting and that simply thresholding the learned gates is harmful. https://github.com/DataScienceUIBK/SmallReason-ColBERT

Accepted at MAIN EMNLP 2026