Towards Efficient Exact Optimization of Language Model Alignment
This addresses the challenge of efficiently optimizing language model alignment for real-world applications, though it appears incremental by improving upon existing methods like DPO.
The paper tackles the problem of aligning language models with human preferences by proposing efficient exact optimization (EXO), which guarantees asymptotic alignment with reinforcement learning while avoiding its high variance, and demonstrates advantages over direct preference optimization (DPO) on realistic data.
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.