ReJump: A Tree-Jump Representation for Analyzing and Improving LLM Reasoning

Yuchen Zeng, Shuibai Zhang, Wonjun Kang, Shutong Wu, Lynnix Zou, Ying Fan, Heeju Kim, Ziqian Lin, Jungtaek Kim, Hyung Il Koo, Dimitris Papailiopoulos, Kangwook Lee

Keywords: Large Reasoning Models, Chain-of-Thought, Reasoning Evaluation, Large Language Models, Model Comparison, Tree-based Analysis

Abstract: Large Language Models (LLMs) typically reason via Chain-of-Thought (CoT) prompting or explicit training. Though many LLMs achieve similar accuracy on challenging tasks, such as math problem solving and programming, how their underlying reasoning "algorithms" compare remains poorly understood. To investigate this, we propose **ReJump**, which represents a reasoning trace as a visitation order over nodes in a tree of intermediate problem-solving steps. ReJump allows *tree jumps*, non-adjacent transitions between nodes that capture reasoning behaviors such as backtracking, verification, and calculation. This representation enables analyzing LLM reasoning with diverse and intuitive metrics that capture exploration, exploitation, overthinking, forgetting, and verification. We apply ReJump to analyze state-of-the-art Large Reasoning Models (LRMs), which are LLMs explicitly trained for long-form CoTs, and find that models with comparable final accuracy can nonetheless display distinct reasoning behaviors. We further compare distilled LRMs with their teachers, CoT-prompted LLMs with LRMs, and investigate how reasoning examples influence reasoning behavior. Finally, we show that ReJump can improve reasoning quality at test time through strategies such as ReJump-guided Best-of-N selection and prompt selection.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 6806