Overview
The evaluation of deep learning models often involves navigating trade-offs among multiple criteria. This tutorial provides a structured overview of gradient-based multi-objective optimization (MOO) for deep learning models. We begin with the foundational theory, systematically exploring three core solution strategies: identifying a single balanced solution, finding a discrete set of Pareto optimal solutions, and learning a continuous Pareto set. We will cover their algorithmic details, convergence, and generalization. The second half of the tutorial focuses on applying MOO to Large Language Models (LLMs). We will demonstrate how MOO offers a principled framework for fine-tuning and aligning LLMs, effectively navigating trade-offs between multiple objectives. Through practical demonstrations of state-of-the-art methods, participants will gain valuable insights. The session will conclude by discussing emerging challenges and future research directions, equipping attendees to tackle multi-objective problems in their work. This tutorial is based on our survey paper Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond.
Speakers
Schedule
Session 1: Introduction and Advanced Methods
Introduction to MOO in Deep Learning
- Motivation and problem formulation
- Key concepts: Pareto optimality, dominance, Pareto front
- Traditional approaches and limitations
Finding a Single Pareto Optimal Solution
- Loss balancing methods
- Gradient weighting methods
- Gradient manipulation methods
- Practical speedup strategies
Finding a Finite Set of Solutions
- Methods based on preference vectors
- Methods without preference vectors
Finding an Infinite Set of Solutions
- Network structures
- Training strategies
Session 2: Theories, Applications, and Beyond
Theoretical Foundations
- Convergence analysis (deterministic vs. stochastic)
- Generalization bounds
Application in LLM
- Multi-objective fine-tuning
- Multi-objective alignment
- Multi-objective test-time alignment
Open Source Libraries
- LibMTL
- LibMOON
Open Challenges and Future Directions
- Theoretical understanding
- Reducing computational costs
- Handling large number of objectives
- Distributed training
Concluding Discussion
Venue
๐ Date & Time: August 29th, 2025
โฑ๏ธ Duration: Two 1:45h slots - Full Afternoon
๐ Location: Langham Place, Guangzhou, China
๐ช Room: TBA
Materials
Contact
For any questions regarding the tutorial, please contact Weiyu Chen.