# Fantastic (small) Retrievers and How to Train Them: mxbai-edge-colbert-v0 Tech Report

arXiv: https://arxiv.org/abs/2510.14880 · HTML: https://mlsanity.com/paper/2510.14880/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2025.

## Built on by (most cited first, 10 of 10)

- [ViDoRe V3: A Comprehensive Evaluation of Retrieval Augmented Generation in Complex Real-World Scenarios](https://mlsanity.com/paper/2601.08620/) (2026)
- [DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search](https://mlsanity.com/paper/2607.27178/) (2026)
- [Benchmarking Optimizers for MLPs in Tabular Deep Learning](https://mlsanity.com/paper/2604.15297/) (2026)
- [ColBERT-Zero: To Pre-train Or Not To Pre-train ColBERT models](https://mlsanity.com/paper/2602.16609/) (2026)
- [Softsign: Smooth Sign in Your Optimizer For Better Parameter Heterogeneity Handling](https://mlsanity.com/paper/2605.31371/) (2026)
- [LEMUR: Learned Multi-Vector Retrieval](https://mlsanity.com/paper/2601.21853/) (2026)
- [EigenLI: Spectral Approximations to Late Interaction](https://mlsanity.com/paper/2609.07561/) (2026)
- [From Evidence-Based Medicine to Knowledge Graph: Retrieval-Augmented Generation for Sports Rehabilitation and a Domain Benchmark](https://mlsanity.com/paper/2601.00216/) (2026)
- [Search over the Visual World: Persistent Visual Memory, Layered Indexes, and Source-Grounded Evidence](https://mlsanity.com/paper/2608.08075/) (2026)
- [ColNanoVDR: Document-Free Query Distillation for Multi-Vector Visual Document Retrieval via Optimal Transport](https://mlsanity.com/paper/2609.34899/) (2026)

## Abstract (arXiv)

In this work, we introduce mxbai-edge-colbert-v0 models, at two different parameter counts: 17M and 32M. As part of our research, we conduct numerous experiments to improve retrieval and late-interaction models, which we intend to distill into smaller models as proof-of-concepts. Our ultimate aim is to support retrieval at all scales, from large-scale retrieval which lives in the cloud to models that can run locally, on any device. mxbai-edge-colbert-v0 is a model that we hope will serve as a solid foundation backbone for all future experiments, representing the first version of a long series of small proof-of-concepts. As part of the development of mxbai-edge-colbert-v0, we conducted multiple ablation studies, of which we report the results. In terms of downstream performance, mxbai-edge-colbert-v0 is a particularly capable small model, outperforming ColBERTv2 on common short-text benchmarks (BEIR) and representing a large step forward in long-context tasks, with unprecedented efficiency.