ARM: Efficient Guided Decoding with Autoregressive Reward Models

Language models trained on large amounts of data require careful tuning to be safely deployed in real world. We revisit the guided decoding paradigm, where the goal is to augment the logits of the base language model using the scores from a task-specific reward model. We propose a simple but efficient parameterization of the autoregressive reward model enabling fast and effective guided decoding. On detoxification and sentiment control tasks, we show that our efficient parameterization performs on par with RAD, a strong but less efficient guided decoding approach. Generative large language models (LLMs) gain a lot of popularity in recent years and show impressive results in zero-shot and few-shot scenarios on numerous downstream tasks (Touvron et al., 2023; OpenAI, 2024; Jiang et al., 2023). These large-scale models are pretrained on large amounts of data, and are known to inherit and memorize the underlying biases (Sheng et al., 2019).