Does Math Reasoning Improve General LLM Capabilities? Understanding
  Transferability of LLM Reasoning
Math reasoning has become the poster child of progress in large language
models (LLMs), with new models rapidly surpassing human-level performance on
benchmarks like MATH and AIME. But as math leaderboards improve week by week,
it is worth asking: do these gains reflect broader problem-solving ability or
just narrow overfitting? To answer this question, we evaluate over 20
open-weight reasoning-tuned models across a broad suite of tasks, including
math, scientific QA, agent planning, coding, and standard
instruction-following. We surprisingly find that most models that succeed in
math fail to transfer their gains to other domains. To rigorously study this
phenomenon, we conduct controlled experiments on Qwen3-14B models using
math-only data but different tuning methods. We find that reinforcement
learning (RL)-tuned models generalize well across domains, while supervised
fine-tuning (SFT)-tuned models often forget general capabilities. Latent-space
representation and token-space distribution shift analyses reveal that SFT
induces substantial representation and output drift, while RL preserves
general-domain structure. Our results suggest a need to rethink standard
post-training recipes, particularly the reliance on SFT-distilled data for
advancing reasoning models.
686490e9d59a9eda59024a64
Maggie Huan
686490e9d59a9eda59024a65
Yuetai Li
686490e9d59a9eda59024a66
Tuney Zheng
claimed_verified
2025-07-02T07:39:59.058Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64ab99dcb76bfd863eba64c1/UBXwDPx17X-gl-SzBPvrc.jpeg
TY.Zheng
user
aaabiao
686490e9d59a9eda59024a67
Xiaoyu Xu
686490e9d59a9eda59024a68
Seungone Kim
686490e9d59a9eda59024a69
Minxin Du
686490e9d59a9eda59024a6a
Radha Poovendran
686490e9d59a9eda59024a6b
Graham Neubig
686490e9d59a9eda59024a6c
Xiang Yue
claimed_verified
2025-07-02T16:07:53.653Z
6230d750d93e84e233882dbc
https://cdn-avatars.huggingface.co/v1/production/uploads/6230d750d93e84e233882dbc/4MGEekLW3oWzqeFWDWvIK.jpeg
Xiang Yue
user
yuexiang96
Does Math Reasoning Improve General LLM Capabilities? Understanding
  Transferability of LLM Reasoning
Math reasoning has become the poster child of progress in large language
models (LLMs), with new models rapidly surpassing human-level performance on
benchmarks like MATH and AIME. But as math leaderboards improve week by week,
it is worth asking: do these gains reflect broader problem-solving ability or
just narrow overfitting? To answer this question, we evaluate over 20
open-weight reasoning-tuned models across a broad suite of tasks, including
math, scientific QA, agent planning, coding, and standard
instruction-following. We surprisingly find that most models that succeed in
math fail to transfer their gains to other domains. To rigorously study this
phenomenon, we conduct controlled experiments on Qwen3-14B models using
math-only data but different tuning methods. We find that reinforcement
learning (RL)-tuned models generalize well across domains, while supervised
fine-tuning (SFT)-tuned models often forget general capabilities. Latent-space
representation and token-space distribution shift analyses reveal that SFT
induces substantial representation and output drift, while RL preserves
general-domain structure. Our results suggest a need to rethink standard
post-training recipes, particularly the reliance on SFT-distilled data for
advancing reasoning models.
Reinforcement learning-tuned models generalize better across domains compared to supervised fine-tuned models in reasoning tasks, indicating a need to reconsider standard training methods.
reinforcement learning supervised fine-tuning latent-space representation token-space distribution shift representation drift output drift general-domain structure