# Goal-Conditioned Supervised Learning for Multi-Objective Recommendation

arXiv: https://arxiv.org/abs/2412.08911 · HTML: https://mlsanity.com/paper/2412.08911/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 41 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 29 of 29)

- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://mlsanity.com/paper/2106.02039/) (2021)
- [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)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Multi-Task Learning as a Bargaining Game](https://mlsanity.com/paper/2202.01017/) (2022)
- [Online Decision Transformer](https://mlsanity.com/paper/2202.05607/) (2022)
- [Goal-Conditioned Reinforcement Learning: Problems and Solutions](https://mlsanity.com/paper/2201.08299/) (2022)
- [Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL](https://mlsanity.com/paper/2209.03993/) (2022)
- [Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL](https://mlsanity.com/paper/2202.04478/) (2022)
- [FAMO: Fast Adaptive Multitask Optimization](https://mlsanity.com/paper/2306.03792/) (2023)
- [Self-Supervised Reinforcement Learning for Recommender Systems](https://mlsanity.com/paper/2006.05779/) (2020)
- [Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems](https://mlsanity.com/paper/2210.10629/) (2022)
- [Multi-Task Recommendations with Reinforcement Learning](https://mlsanity.com/paper/2302.03328/) (2023)
- [Reinformer: Max-Return Sequence Modeling for Offline RL](https://mlsanity.com/paper/2405.08740/) (2024)
- [Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2110.15097/) (2021)
- [Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective](https://mlsanity.com/paper/2206.07353/) (2022)
- [Robust Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2309.02057/) (2023)
- [Pareto-based Multi-Objective Recommender System with Forgetting Curve](https://mlsanity.com/paper/2312.16868/) (2023)
- [Constrained Reinforcement Learning for Short Video Recommendation](https://mlsanity.com/paper/2205.13248/) (2022)

## Built on by (most cited first, 1 of 1)

- [Recommendation World Models for Future-State Control](https://mlsanity.com/paper/2609.30711/) (2026)

## Abstract (arXiv)

Multi-objective learning endeavors to concurrently optimize multiple objectives using a single model, aiming to achieve high and balanced performance across diverse objectives. However, this often entails a complex optimization problem to balance the learning of potentially conflicting objectives, leading to solutions with higher memory requirements and computational complexity. This paper introduces a Multi-Objective Goal-Conditioned Supervised Learning (MOGCSL) framework for automatically learning to achieve multiple objectives from offline sequential data. MOGCSL extends the conventional GCSL method to multi-objective scenarios by redefining goals from one-dimensional scalars to multi-dimensional vectors. It benefits from naturally eliminating the need for complex architectures and optimization constraints. Moreover, MOGCSL inherently disentangles uninformative or noisy training instances that fail to achieve desirable long-term rewards across multiple objectives. We also introduce a novel goal-selection algorithm for MOGCSL to model and identify desired and achievable goals for inference. In this paper, we focus on its application to the next action prediction problem in commercial-grade recommender systems. In this context, any viable solution needs to be reasonably scalable and also be robust to large amounts of noisy data that is characteristic of this application space. We show that MOGCSL performs admirably on both counts by extensive experiments. Also, analysis and experiments are included to explain its strength in discounting the noisier portions of training data in recommender systems with multiple objectives.