Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory
  and Test-Time Compute Scaling
Authors: name: Ivan Rodkin, name: Daniil Orel, name: Konstantin Smirnov, name: Arman Bolatov, name: Bilal Elbouardi, name: Besher Hassan, name: Yuri Kuratov, name: Aydar Bulatov, name: Preslav Nakov, name: Timothy Baldwin, name: Artem Shelmanov, name: Mikhail Burtsev

Reasoning is a core capability of large language models, yet understanding
how they learn and perform multi-step reasoning remains an open problem. In
this study, we explore how different architectures and training methods affect
model multi-step reasoning capabilities within a cellular automata framework.
By training on state sequences generated with random Boolean functions for
random initial conditions to exclude memorization, we demonstrate that most
neural architectures learn to abstract the underlying rules. While models
achieve high accuracy in next-state prediction, their performance declines
sharply if multi-step reasoning is required. We confirm that increasing model
depth plays a crucial role for sequential computations. We demonstrate that an
extension of the effective model depth with recurrence, memory, and test-time
compute scaling substantially enhances reasoning capabilities.