Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models
Year: 2026
Type: conference-paper
Abstract: Instruction-following is essential for aligning large language models (LLMs) with user intent.Yet recent reasoning-oriented models, despite their strong performance on complex mathematical problems, often fail to comply with simple natural language directives.In this work, we analyze the interaction between reasoning ability and instruction adherence in large reasoning models (LRMs).Using a controlled evaluation framework (MathIF), we uncover a persistent trade-off: as models scale reasoning capacity through long chains-of-thought or reinforcement learning on reasoning traces, their obedience to instructions degrades, particularly when generation length grows.We further show that interventions such as constraining or repeating instructions can partially restore compliance, but typically at the expense of reasoning performance.Taken together, our findings expose a dilemma between intelligence and obedience in current training paradigms and underscore the need for instruction-aware approaches to developing controllable reasoning models.
Authors: Tingchen Fu, Yafu Li, Jiawei Gu, Xiaoye Qu, Yu Cheng
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414580661