Rules, Not Weights (danieltan.weblog.lol)

A new approach in machine learning explores the idea of "searching the rule" instead of solely training weights, as seen in traditional models. While mainstream ML relies on fixed scoring rules, such as softmax and cross-entropy, this innovation enables researchers to manipulate scoring rules as dynamic entities within the training process. By employing a symbolic engine, rules are treated as adjustable terms, allowing for efficient comparison, mutation, and caching, thus transforming rule-search into a more accessible and less cumbersome task. This methodology can lead to the development of rules that a designer might not have initially considered. The significance of this development lies in its potential to improve ML efficiency and performance across various tasks. The engine allows for rapid iterations of scoring rules without the need for extensive code changes, thereby streamlining experimentation. Experimental results indicate promising advancements in areas like fact retrieval and language generation, where the system demonstrated notable improvements over traditional baselines. This approach also highlights the importance of scoring rules in shaping machine learning outcomes, suggesting that effective rule design can lead to enhanced model performance in real-world applications.