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SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms
Alex Havrilla, Edward Hughes, Mikayel Samvelyan +1
Large language model (LLM) driven synthetic data generation has emerged as a powerful method for improving model reasoning capabilities. However, most methods either distill large…
Can Transformers Reason Logically? A Study in SAT Solving
Leyan Pan, Vijay Ganesh, Jacob Abernethy +2
We formally study the logical reasoning capabilities of decoder-only Transformers in the context of the boolean satisfiability (SAT) problem. First, we prove by construction that d…
Faster Margin Maximization Rates for Generic and Adversarially Robust Optimization Methods
Guanghui Wang, Zihao Hu, Claudio Gentile +2
First-order optimization methods tend to inherently favor certain solutions over others when minimizing an underdetermined training objective that has multiple global optima. This…