# LibMTL: A Python Library for Multi-Task Learning

arXiv: https://arxiv.org/abs/2203.14338 · HTML: https://mlsanity.com/paper/2203.14338/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 27 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [Conflict-Averse Gradient Descent for Multi-task Learning](https://mlsanity.com/paper/2110.14048/) (2021)
- [Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout](https://mlsanity.com/paper/2010.06808/) (2020)
- [Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models](https://mlsanity.com/paper/2010.05874/) (2020)
- [DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning](https://mlsanity.com/paper/2106.03760/) (2021)
- [MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning](https://mlsanity.com/paper/1904.08492/) (2019)

## Built on by (most cited first, 25 of 25)

- [Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance](https://mlsanity.com/paper/2305.20057/) (2023)
- [Addressing Negative Transfer in Diffusion Models](https://mlsanity.com/paper/2306.00354/) (2023)
- [ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning](https://mlsanity.com/paper/2301.12618/) (2023)
- [Dual-Balancing for Multi-Task Learning](https://mlsanity.com/paper/2308.12029/) (2023)
- [Robust Multi-Task Learning with Excess Risks](https://mlsanity.com/paper/2402.02009/) (2024)
- [Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach](https://mlsanity.com/paper/2210.12624/) (2022)
- [A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization](https://mlsanity.com/paper/2401.09257/) (2024)
- [Multi-domain Recommendation with Embedding Disentangling and Domain Alignment](https://mlsanity.com/paper/2308.05508/) (2023)
- [Sample-Level Weighting for Multi-Task Learning with Auxiliary Tasks](https://mlsanity.com/paper/2306.04519/) (2023)
- [When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review](https://mlsanity.com/paper/2307.14382/) (2023)
- [Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning](https://mlsanity.com/paper/2408.07985/) (2024)
- [Multitask Learning in Minimally Invasive Surgical Vision: A Review](https://mlsanity.com/paper/2401.08256/) (2024)
- [radarODE-MTL: A Multi-Task Learning Framework with Eccentric Gradient Alignment for Robust Radar-Based ECG Reconstruction](https://mlsanity.com/paper/2410.08656/) (2024)
- [Label-free Neural Semantic Image Synthesis](https://mlsanity.com/paper/2407.01790/) (2024)
- [Federated Communication-Efficient Multi-Objective Optimization](https://mlsanity.com/paper/2410.16398/) (2024)
- [Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal](https://mlsanity.com/paper/2305.00441/) (2023)
- [Examining Common Paradigms in Multi-Task Learning](https://mlsanity.com/paper/2311.04698/) (2023)
- [Multi-Task Learning as enabler for General-Purpose AI-native RAN](https://mlsanity.com/paper/2404.15197/) (2024)
- [EndoARSS: Adapting Spatially-Aware Foundation Model for Efficient Activity Recognition and Semantic Segmentation in Endoscopic Surgery](https://mlsanity.com/paper/2506.06830/) (2025)
- [SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation](https://mlsanity.com/paper/2507.07883/) (2025)
- [Simplifying Multi-Task Architectures Through Task-Specific Normalization](https://mlsanity.com/paper/2512.20420/) (2025)
- [Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference](https://mlsanity.com/paper/2608.18982/) (2026)
- [Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis](https://mlsanity.com/paper/2406.07991/) (2024)
- [HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis](https://mlsanity.com/paper/2604.03224/) (2026)
- [Towards Unified Multi-task EEG Analysis with Low-Rank Adaptation](https://mlsanity.com/paper/2604.25131/) (2026)

## Abstract (arXiv)

This paper presents LibMTL, an open-source Python library built on PyTorch, which provides a unified, comprehensive, reproducible, and extensible implementation framework for Multi-Task Learning (MTL). LibMTL considers different settings and approaches in MTL, and it supports a large number of state-of-the-art MTL methods, including 12 loss weighting strategies, 7 architectures, and 84 combinations of different architectures and loss weighting methods. Moreover, the modular design in LibMTL makes it easy-to-use and well extensible, thus users can easily and fast develop new MTL methods, compare with existing MTL methods fairly, or apply MTL algorithms to real-world applications with the support of LibMTL. The source code and detailed documentations of LibMTL are available at https://github.com/median-research-group/LibMTL and https://libmtl.readthedocs.io, respectively.