Rethinking Multi-Objective Learning through Goal-Conditioned Supervised Learning

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Abstract:Multi-objective learning aims to optimize multiple objectives simultaneously with a single model for achieving a balanced and satisfying performance on all these objectives. However, it suffers from the difficulty to formalize and conduct the exact learning process, especially considering the possible conflicts between objectives. Existing approaches explores to resolve this primarily in two directions: adapting modeling structure or constraining optimization with certain assumptions. However, a primary issue is that their presuppositions for the effectiveness of their design are insufficient to guarantee the its generality in real-world applications. What’s worse, the high space and computation complexity issue makes it even harder to apply them in large-scale, complicated environment such as the recommender systems. To address these issues, we propose a general framework for automatically learning to achieve multiple objectives based on the existing sequential data. We apply the goal-conditioned supervised learning (GCSL) framework to multi-objective learning, by extending the definition of goals from one-dimensional scalar to multi-dimensional vector that perfectly disentangle the representation of different objectives. Meanwhile, GCSL enables the model to simultaneously learn to achieve each objective in a concise supervised learning way, simply guided by existing sequences in the offline data. No additional constraint, special model structure design, or complex optimization algorithms are further required. Apart from that, we formally analyze the property of the goals in GCSL and then firstly propose a goal-generation framework to gain achievable and reasonable goals for inference. Extensive experiments are conducted on real-world recommendation datasets, demonstrating the effectiveness of the proposed method and exploring the feasibility of the goal-generation strategies in GCSL.