Inference-Time Language Model Alignment

The field studies how to guide a language model toward desired behavior at generation time, often without changing its weights. Approaches include prompt and persona conditioning, activation or logit steering, reward-guided decoding, and sampling or reranking methods such as Best-of-N. Researchers apply these methods to safety, helpfulness, reasoning, personalization, and conflicting preferences, while developing theoretical guarantees and more efficient ways to evaluate them. A central challenge is balancing gains in alignment against compute cost, reward-model errors, loss of useful capabilities, and the difficulty of transferring results across tasks and conversations.