Surpassing Frontier Performance with Fusion

Fusion is a tool designed to enhance model performance by synthesizing the results of multiple models, allowing them to collectively surpass the capabilities of individual models. By implementing a panel of participant models alongside a judge model, Fusion synthesizes different perspectives to tackle complex problems, as demonstrated through testing on the DRACO benchmark, which evaluates reasoning, tool usage, and knowledge. The process involves parallel dispatching of prompts to models, with web search capabilities, followed by a judge model producing structured analyses, leading to a final answer that amalgamates these insights. Notably, Fusion showcases the ability of budget model panels to perform close to high-cost frontier models, and even combining a model with itself results in performance gains, indicating the significance of the synthesis step. The implementation of DRACO, despite some limitations, highlights the effectiveness of Fusion in achieving superior results through model diversity and structured synthesis, while measures are taken to prevent models from accessing grading rubrics and ensuring fair evaluations.

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