Scaling Reasoning, Losing Control: Evaluating Instruction Following in
  Large Reasoning Models
Instruction-following is essential for aligning large language models (LLMs)
with user intent. While recent reasoning-oriented models exhibit impressive
performance on complex mathematical problems, their ability to adhere to
natural language instructions remains underexplored. In this work, we introduce
MathIF, a dedicated benchmark for evaluating instruction-following in
mathematical reasoning tasks. Our empirical analysis reveals a consistent
tension between scaling up reasoning capacity and maintaining controllability,
as models that reason more effectively often struggle to comply with user
directives. We find that models tuned on distilled long chains-of-thought or
trained with reasoning-oriented reinforcement learning often degrade in
instruction adherence, especially when generation length increases.
Furthermore, we show that even simple interventions can partially recover
obedience, though at the cost of reasoning performance. These findings
highlight a fundamental tension in current LLM training paradigms and motivate
the need for more instruction-aware reasoning models. We release the code and
data at https://github.com/TingchenFu/MathIF.
682ea2b450671dc82688b8ad
Tingchen Fu
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2025-05-22T07:15:44.217Z
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Tingchen Fu
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TingchenFu
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Jiawei Gu
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2025-05-26T08:21:57.596Z
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Jiawei Gu
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Yafu Li
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2025-05-22T04:06:13.396Z
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Yafu Li
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Xiaoye Qu
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2025-05-26T08:21:28.583Z
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Xiaoye Qu
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Yu Cheng
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2025-06-02T07:49:34.787Z
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Yu Cheng
user
ych133
Scaling Reasoning, Losing Control: Evaluating Instruction Following in
  Large Reasoning Models
Instruction-following is essential for aligning large language models (LLMs)
with user intent. While recent reasoning-oriented models exhibit impressive
performance on complex mathematical problems, their ability to adhere to
natural language instructions remains underexplored. In this work, we introduce
MathIF, a dedicated benchmark for evaluating instruction-following in
mathematical reasoning tasks. Our empirical analysis reveals a consistent
tension between scaling up reasoning capacity and maintaining controllability,
as models that reason more effectively often struggle to comply with user
directives. We find that models tuned on distilled long chains-of-thought or
trained with reasoning-oriented reinforcement learning often degrade in
instruction adherence, especially when generation length increases.
Furthermore, we show that even simple interventions can partially recover
obedience, though at the cost of reasoning performance. These findings
highlight a fundamental tension in current LLM training paradigms and motivate
the need for more instruction-aware reasoning models. We release the code and
data at https://github.com/TingchenFu/MathIF.
An empirical analysis of MathIF identifies a tension between enhancing reasoning capacity and maintaining instruction adherence in large language models.
instruction-following reasoning-oriented models benchmarks chains-of-thought reinforcement learning instruction adherence