Continual Optimization with Symmetry Teleportation for Multi-Task Learning

• URL: http://arxiv.org/abs/2503.04046v1
• Date: Thu, 06 Mar 2025 02:58:09 GMT
• Title: Continual Optimization with Symmetry Teleportation for Multi-Task Learning
• Authors: Zhipeng Zhou, Ziqiao Meng, Pengcheng Wu, Peilin Zhao, Chunyan Miao,
• Abstract summary: Multi-task learning (MTL) enables the simultaneous learning of multiple tasks using a single model.<n>We propose a novel approach based on Continual Optimization with Symmetry Teleportation (COST)<n>COST seeks an alternative loss-equivalent point on the loss landscape to reduce conflict gradients.
• Score: 73.28772872740744
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-task learning (MTL) is a widely explored paradigm that enables the simultaneous learning of multiple tasks using a single model. Despite numerous solutions, the key issues of optimization conflict and task imbalance remain under-addressed, limiting performance. Unlike existing optimization-based approaches that typically reweight task losses or gradients to mitigate conflicts or promote progress, we propose a novel approach based on Continual Optimization with Symmetry Teleportation (COST). During MTL optimization, when an optimization conflict arises, we seek an alternative loss-equivalent point on the loss landscape to reduce conflict. Specifically, we utilize a low-rank adapter (LoRA) to facilitate this practical teleportation by designing convergent, loss-invariant objectives. Additionally, we introduce a historical trajectory reuse strategy to continually leverage the benefits of advanced optimizers. Extensive experiments on multiple mainstream datasets demonstrate the effectiveness of our approach. COST is a plug-and-play solution that enhances a wide range of existing MTL methods. When integrated with state-of-the-art methods, COST achieves superior performance.

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