Enhancing Best-of-N Decoding by Speculative Rejection and Self-Certainty - Gouvéa Junior, Jose Lamir; Garcia, Luan Fonseca; Oliveira, Ewerton de; Paula, Thomas (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS), 2025)
Controllable text generation techniques such as fine-tuning, reinforcement learning, and prompt engineering have significant potential to enhance reasoning, alignment, and efficiency in Large Language Models. However, these methods often struggle with memory management, generalization across diverse language tasks, and score function design. In contrast, enhancing the decoding process has proven to be an effective way to control generation without requiring additional training or external tools. This work proposes an improved parallel decoding strategy that not only alleviates resource requirements but also effectively leverages its guiding reward function.