A recent study published on arXiv investigates the comparative effectiveness of inverse-entropy voting versus parallel self-consistency methods in language model reasoning. The research evaluated five advanced open-source language models across three challenging benchmarks to assess performance. Findings indicate that sequential scaling, defined as a process where reasoning chains build upon previous outputs rather than running multiple independent chains in parallel, yields superior results. This sequential approach demonstrated a clear advantage in reasoning accuracy and efficiency. The study suggests that inverse-entropy voting, which leverages this sequential methodology, outperforms parallel self-consistency when compute resources are matched. Such improvements could have significant implications for enhancing the efficiency and capability of language models in various applications. These results align with ongoing research trends emphasizing the benefits of sequential reasoning strategies in artificial intelligence.