Zapier’s Mike Knoop launches ARC Prize to Jumpstart New Ideas for AGI
Training Data
Exploring Different Techniques for Improving Puzzle Solving Success Rates
Researchers have developed two key techniques to improve puzzle solving success rates. The first technique involves creating a domain-specific language (DSL) based on observed patterns in public test sets, subsequently using brute force search to identify general patterns and apply them in real-time, achieving a 30% success rate. The second technique by Jack Cole utilizes a code-based open-source language model with test-time fine-tuning, achieving a 40% success rate. The future potential lies in combining existing ideas for a 50% success rate, and further advancements towards 85% or beyond may involve a deep learning guided dynamically generated DSL, incorporating deep learning-based program synthesis engines. These methods mimic human puzzle-solving behavior by focusing on likely candidates and testing determinants instead of brute-forcing all possible programs.
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