Selection, Reflection and Self-Refinement: Revisit Reasoning Tasks via a Causal Lens
Summary: arXiv:2510.08222v2 Announce Type: replace
Abstract
Due to their inherent complexity, reasoning tasks have long been regarded as rigorous benchmarks for assessing the capabilities of machine learning models, especially large language models (LLMs). Although humans can solve these tasks with ease, existing models, even after extensive pre-training and post-training at scale, still fail to perform reasoning reliably. In this paper, we revisit reasoning tasks from a causal perspective, seeking to understand their behavior in latent space and to offer insights for addressing their challenges.
Understanding Reasoning Tasks
Specifically, we cast reasoning tasks as a selection mechanism, in which high-level logical concepts function as selection operators on the given observations, such as identifying the correct answer in a math problem or filling the appropriate entry in Sudoku. We emphasize two key properties of this formulation that shed light on the difficulty of reasoning tasks:
- Complexity of Latent Space: The latent space exceeds the observation space in complexity, even when the correct answer is fully determined by the observed input.
- Density and Dependencies: The latent variables, corresponding to logical thought, are densely structured and exhibit strong dependencies.
Introducing the SR2 Framework
Building on this formulation, we introduce a framework, called SR2, that incorporates the estimated latent variables as feedback into the selection mechanism, thereby facilitating the learning of dense dependencies among latent representations. The framework consists of three key modules:
- Reflective Representation Learning: This module aims to enhance the understanding of latent variables through reflective processes that capture deeper insights.
- Dependency Self-Refinement: This component focuses on refining the relationships among latent variables to ensure more robust reasoning capabilities.
- Periodic Intermediate Alignment: This module facilitates the alignment of intermediate representations, ensuring consistency and coherence in reasoning tasks.
Experimental Insights
Experimentally, we show that our approach yields significant gains in reasoning accuracy. For instance, we attained over 10% improvement in performance with 8× fewer parameters on the Sudoku and Maze tasks compared to recent advances. This demonstrates the efficacy of the SR2 framework in addressing the challenges presented by reasoning tasks.
Conclusion
The exploration of reasoning tasks through a causal lens opens new pathways for enhancing machine learning models, particularly large language models. By implementing the SR2 framework, we pave the way for more effective reasoning capabilities, potentially bridging the gap between human-like reasoning and machine learning performance.