Inference-Time Nash Alignment
Preference-based fine-tuning methods such as RLHF and DPO require substantial compute and large preference datasets. They also need direct access to the model parameters which are not provided by many state-of-the art models. Inference-time alignment offers a cost-effective alternative without updating model parameters. However, existing inference-time methods rely on a scalar reward model derived under a Bradley-Terry assumption, which cannot represent general preferences. Following recent work on fine-tuning with generalized preferences, in this work, we initiate the study of inference-time alignment under general preferences. We formulate the problem as obtaining a Nash equilibrium of ...
| Authors | Hadi Hosseini, Debmalya Mandal, Duohan Zhang |
|---|---|
| Primary category | cs.AI |
| Published | 8 September 2026 |
| Last updated at source | 16 September 2026 |
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