Fast Best-of-N Decoding via Speculative Rejection
Authors: name: Hanshi Sun, name: Momin Haider, name: Ruiqi Zhang, name: Huitao Yang, name: Jiahao Qiu, name: Ming Yin, name: Mengdi Wang, name: Peter Bartlett, name: Andrea Zanette

The safe and effective deployment of Large Language Models (LLMs) involves a
critical step called alignment, which ensures that the model's responses are in
accordance with human preferences. Prevalent alignment techniques, such as DPO,
PPO and their variants, align LLMs by changing the pre-trained model weights
during a phase called post-training. While predominant, these post-training
methods add substantial complexity before LLMs can be deployed. Inference-time
alignment methods avoid the complex post-training step and instead bias the
generation towards responses that are aligned with human preferences. The
best-known inference-time alignment method, called Best-of-N, is as effective
as the state-of-the-art post-training procedures. Unfortunately, Best-of-N
requires vastly more resources at inference time than standard decoding
strategies, which makes it computationally not viable. In this work, we
introduce Speculative Rejection, a computationally-viable inference-time
alignment algorithm. It generates high-scoring responses according to a given
reward model, like Best-of-N does, while being between 16 to 32 times more
computationally efficient.