Summary of Rethinking Multi-objective Learning Through Goal-conditioned Supervised Learning, by Shijun Li et al.
Rethinking Multi-Objective Learning through Goal-Conditioned Supervised Learning
by Shijun Li, Hilaf Hasson, Jing Hu, Joydeep Ghosh
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to multi-objective learning, which aims to optimize multiple objectives simultaneously with a single model for achieving a balanced performance. The existing approaches suffer from difficulties in formalizing and conducting the exact learning process, especially considering potential conflicts between objectives. To address this issue, the authors introduce a general framework for automatically learning to achieve multiple objectives based on sequential data. They 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 vectors. This approach enables the model to simultaneously learn to achieve each objective in a concise way, guided by existing sequences in offline data. The proposed method does not require additional constraints, special model structures, or complex optimization algorithms. The authors also formally analyze the properties of goals in GCSL and propose a goal-generation framework to gain achievable and reasonable goals for inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a way to teach a machine learning model to do many things at once. Right now, it’s hard to make models that can do this because it’s difficult to come up with the right plan for making them learn. The authors are proposing a new approach that uses something called goal-conditioned supervised learning (GCSL) to help the model figure out how to do all these things at once. They’re also talking about ways to make sure the goals they set are reasonable and achievable. This could be important for things like recommending movies or music, where we want a model that can take into account lots of different factors. |
Keywords
» Artificial intelligence » Inference » Machine learning » Optimization » Supervised
