FINEREASON: Evaluating and Improving LLMs' Deliberate Reasoning through
  Reflective Puzzle Solving
Authors: name: Guizhen Chen, name: Weiwen Xu, name: Hao Zhang, name: Hou Pong Chan, name: Chaoqun Liu, name: Lidong Bing, name: Deli Zhao, name: Anh Tuan Luu, name: Yu Rong

Many challenging reasoning tasks require not just rapid, intuitive responses,
but a more deliberate, multi-step approach. Recent progress in large language
models (LLMs) highlights an important shift from the "System 1" way of quick
reactions to the "System 2" style of reflection-and-correction problem solving.
However, current benchmarks heavily rely on the final-answer accuracy, leaving
much of a model's intermediate reasoning steps unexamined. This fails to assess
the model's ability to reflect and rectify mistakes within the reasoning
process. To bridge this gap, we introduce FINEREASON, a logic-puzzle benchmark
for fine-grained evaluation of LLMs' reasoning capabilities. Each puzzle can be
decomposed into atomic steps, making it ideal for rigorous validation of
intermediate correctness. Building on this, we introduce two tasks: state
checking, and state transition, for a comprehensive evaluation of how models
assess the current situation and plan the next move. To support broader
research, we also provide a puzzle training set aimed at enhancing performance
on general mathematical tasks. We show that models trained on our state
checking and transition data demonstrate gains in math reasoning by up to 5.1%
on GSM8K.