Title: Structured Reasoning for LLMs: A Unified Framework for Efficiency and Explainability
Primary area: reinforcement learning
Keywords: Large Language Models, Reasoning, Chain of thought, Reinforcement Learning, Neurosymbolic AI, Interpretability

Abstract: Recent Large Language Models (LLMs) have made remarkable progress, but they still struggle with complex reasoning tasks such as logical deduction and planning. This is partly because they rely primarily on token-level probability relationships, which limits their ability to reason effectively. 
In this paper, inspired by cognitive science and neurosymbolic AI, we introduce Structured Reasoning, which aimes at enhancing the reasoning capabilities of LLMs from the step level. 
To this end, we first collect high‑frequency, domain‑agnostic reasoning step tags and  construct a structured reasoning dataset with those tags. 
Then, we treat a reasoning process as a directed graph, where the vertices represent steps and the edges indicate the direction of reasoning. 
In this context, an efficient reasoning process corresponds to, or can be characterized by, a sparse reasoning graph.  
1) To construct such a graph, we propose MAX-Flow, which maximizes step-to-step attention flow while minimizing the number of edges. The quality of a sparse reasoning graph can be reflected by the total flow from all steps to the final answer.
2) To improve the graph, we propse LCS (Longest Common Sequence), which selects reliable reasoning paths by identifying optimal common subsequences (consecutive steps) shared across multiple generated responses (sequences).  
Experiments with DeepSeek-R1-Distill-Qwen-1.5B and 7B models show that our method  consistently outperforms GRPO and other carefully tuned baselines across various context lengths (0.5k–8k).
Structured Reasoning shows particular strength in efficiency (better performance with fewer steps) and stability (consistently generating high-quality outputs across a temperature range of 0.1 to 1.0).