# RepDL: Bit-level Reproducible Deep Learning Training and Inference

arXiv: https://arxiv.org/abs/2510.09180 · HTML: https://mlsanity.com/paper/2510.09180/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [Randomness In Neural Network Training: Characterizing The Impact of Tooling](https://mlsanity.com/paper/2106.11872/) (2021)
- [Nondeterminism and Instability in Neural Network Optimization](https://mlsanity.com/paper/2103.04514/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction](https://mlsanity.com/paper/2603.21698/) (2026)
- [Bit-Identical Medical Deep Learning via Structured Orthogonal Initialization](https://mlsanity.com/paper/2603.28040/) (2026)

## Abstract (arXiv)

Non-determinism and non-reproducibility present significant challenges in deep learning, leading to inconsistent results across runs and platforms. These issues stem from two origins: random number generation and floating-point computation. While randomness can be controlled through deterministic configurations, floating-point inconsistencies remain largely unresolved. To address this, we introduce RepDL, an open-source library that ensures deterministic and bitwise-reproducible deep learning training and inference across diverse computing environments. RepDL achieves this by enforcing correct rounding and order invariance in floating-point computation. The source code is available at https://github.com/microsoft/RepDL .