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Towards Efficient Exact Optimization of Language Model Alignment
- Source :
- Forty-first International Conference on Machine Learning (ICML 2024)
- Publication Year :
- 2024
-
Abstract
- The alignment of language models with human preferences is vital for their application in real-world tasks. The problem is formulated as optimizing the model's policy to maximize the expected reward that reflects human preferences with minimal deviation from the initial policy. While considered as a straightforward solution, reinforcement learning (RL) suffers from high variance in policy updates, which impedes efficient policy improvement. Recently, direct preference optimization (DPO) was proposed to directly optimize the policy from preference data. However, we show that DPO derived based on the optimal solution of the problem leads to a compromised mean-seeking approximation of the optimal solution in practice. In this paper, we propose efficient exact optimization (EXO) of the alignment objective. EXO is guaranteed to optimize in the same direction as RL algorithms asymptotically for arbitrary policy parametrization. This leads to the same mode-seeking solution, while enables efficient optimization by circumventing the complexities of RL. We also compare our method to DPO with both theoretical and empirical analyses, and further demonstrate the advantages of our method over existing approaches on realistic human preference data. Code is available at https://github.com/haozheji/exact-optimization.<br />Comment: 24 pages, 9 figures
- Subjects :
- Computer Science - Computation and Language
Subjects
Details
- Database :
- arXiv
- Journal :
- Forty-first International Conference on Machine Learning (ICML 2024)
- Publication Type :
- Report
- Accession number :
- edsarx.2402.00856
- Document Type :
- Working Paper