ScaleDiff: Scaling Difficult Problems for Advanced Mathematical
  Reasoning
Large Reasoning Models (LRMs) have shown impressive capabilities in complex
problem-solving, often benefiting from training on difficult mathematical
problems that stimulate intricate reasoning. Recent efforts have explored
automated synthesis of mathematical problems by prompting proprietary models or
large-scale open-source models from seed data or inherent mathematical
concepts. However, scaling up these methods remains challenging due to their
high computational/API cost, complexity of prompting, and limited difficulty
level of the generated problems. To overcome these limitations, we propose
ScaleDiff, a simple yet effective pipeline designed to scale the creation of
difficult problems. We efficiently identify difficult problems from existing
datasets with only a single forward pass using an adaptive thinking model,
which can perceive problem difficulty and automatically switch between
"Thinking" and "NoThinking" modes. We then train a specialized difficult
problem generator (DiffGen-8B) on this filtered difficult data, which can
produce new difficult problems in large scale, eliminating the need for
complex, per-instance prompting and its associated high API costs. Fine-tuning
Qwen2.5-Math-7B-Instruct on the ScaleDiff-Math dataset yields a substantial
performance increase of 11.3% compared to the original dataset and achieves a
65.9% average accuracy on AIME'24, AIME'25, HMMT-Feb'25, BRUMO'25, and MATH500,
outperforming recent strong LRMs like OpenThinker3. Notably, this performance
is achieved using the cost-efficient Qwen3-8B model as a teacher, demonstrating
that our pipeline can effectively transfer advanced reasoning capabilities
without relying on larger, more expensive teacher models. Furthermore, we
observe a clear scaling phenomenon in model performance on difficult benchmarks
as the quantity of difficult problems increases. Code:
https://github.com/QizhiPei/ScaleDiff.
68d5fa038ccd91bdd39ffdc7
Qizhi Pei
admin_assigned
2025-09-26T16:20:12.185Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6397f6081323f19c578f142e/it7FYYKjlLX8wSsMLm8EO.jpeg
QizhiPei
user
QizhiPei
68d5fa038ccd91bdd39ffdc8
Zhuoshi Pan
admin_assigned
2025-09-26T16:20:20.007Z
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/avatars/0d7cf0e1b42cd116960cd030478446c5.svg
Zhuoshi Pan
user
panzs19
68d5fa038ccd91bdd39ffdc9
Honglin Lin
admin_assigned
2025-09-26T16:20:26.362Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/640d99628512ec51d7ef71c7/fcBkqnxfxuuuZTqfN_BGy.jpeg
Honglin Lin
user
LHL3341
68d5fa038ccd91bdd39ffdca
Xin Gao
68d5fa038ccd91bdd39ffdcb
Yu Li
68d5fa038ccd91bdd39ffdcc
Zinan Tang
admin_assigned
2025-09-26T16:20:33.204Z
66580d3d80ee5b1e11a94e57
https://cdn-avatars.huggingface.co/v1/production/uploads/66580d3d80ee5b1e11a94e57/aHaPqrV5vNefFktYRsiGf.jpeg
Zinan Tang
user
Word2Li
68d5fa038ccd91bdd39ffdcd
Conghui He
admin_assigned
2025-09-26T16:20:39.392Z
63f9fca8d4349b157a109eec
/avatars/fa1f2ae7972d7cde99dab178136ccbb0.svg
Conghui He
user
conghui
68d5fa038ccd91bdd39ffdce
Rui Yan
68d5fa038ccd91bdd39ffdcf
Lijun Wu
ScaleDiff: Scaling Difficult Problems for Advanced Mathematical
  Reasoning
Large Reasoning Models (LRMs) have shown impressive capabilities in complex
problem-solving, often benefiting from training on difficult mathematical
problems that stimulate intricate reasoning. Recent efforts have explored
automated synthesis of mathematical problems by prompting proprietary models or
large-scale open-source models from seed data or inherent mathematical
concepts. However, scaling up these methods remains challenging due to their
high computational/API cost, complexity of prompting, and limited difficulty
level of the generated problems. To overcome these limitations, we propose
ScaleDiff, a simple yet effective pipeline designed to scale the creation of
difficult problems. We efficiently identify difficult problems from existing
datasets with only a single forward pass using an adaptive thinking model,
which can perceive problem difficulty and automatically switch between
"Thinking" and "NoThinking" modes. We then train a specialized difficult
problem generator (DiffGen-8B) on this filtered difficult data, which can
produce new difficult problems in large scale, eliminating the need for
complex, per-instance prompting and its associated high API costs. Fine-tuning
Qwen2.5-Math-7B-Instruct on the ScaleDiff-Math dataset yields a substantial
performance increase of 11.3% compared to the original dataset and achieves a
65.9% average accuracy on AIME'24, AIME'25, HMMT-Feb'25, BRUMO'25, and MATH500,
outperforming recent strong LRMs like OpenThinker3. Notably, this performance
is achieved using the cost-efficient Qwen3-8B model as a teacher, demonstrating
that our pipeline can effectively transfer advanced reasoning capabilities
without relying on larger, more expensive teacher models. Furthermore, we
observe a clear scaling phenomenon in model performance on difficult benchmarks
as the quantity of difficult problems increases. Code:
https://github.com/QizhiPei/ScaleDiff.
ScaleDiff uses an adaptive thinking model to identify and generate difficult mathematical problems, improving the performance of large reasoning models with cost-efficient training.
Large Reasoning Models LRMs automated synthesis mathematical problems adaptive thinking model Thinking mode NoThinking mode DiffGen-8B Qwen2.5-Math-7B-Instruct ScaleDiff-Math dataset AIME HMMT-Feb BRUMO MATH500 OpenThinker3 Qwen3-8B