InfinityMATH: A Scalable Instruction Tuning Dataset in Programmatic
  Mathematical Reasoning
Recent advancements in Chain-of-Thoughts (CoT) and Program-of-Thoughts (PoT)
methods have greatly enhanced language models' mathematical reasoning
capabilities, facilitating their integration into instruction tuning datasets
with LLMs. However, existing methods for large-scale dataset creation require
substantial seed data and high computational costs for data synthesis, posing
significant challenges for scalability. We introduce InfinityMATH, a scalable
instruction tuning dataset for programmatic mathematical reasoning. The
construction pipeline emphasizes decoupling numbers from mathematical problems
to synthesize number-independent programs, enabling efficient and flexible
scaling while minimizing dependency on specific numerical values. Fine-tuning
experiments with open-source language and code models, such as Llama2 and
CodeLlama, demonstrate the practical benefits of InfinityMATH. These fine-tuned
models, showed significant relative improvements on both in-domain and
out-of-domain benchmarks, ranging from 184.7% to 514.3% on average.
Additionally, these models exhibited high robustness on the GSM8K+ and MATH+
benchmarks, which are enhanced version of test sets with simply the number
variations. InfinityMATH ensures that models are more versatile and effective
across a broader range of mathematical problems. The data is available at
https://huggingface.co/datasets/flagopen/InfinityMATH.
66bd603b3f0c44697d3eba36
Bo-Wen Zhang
claimed_verified
2024-08-19T22:13:50.084Z
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/avatars/aa1837a3a07514b89c48de888934a7b2.svg
bowenzhang
user
bowen92
66bd603b3f0c44697d3eba37
Yan Yan
66bd603b3f0c44697d3eba38
Lin Li
extracted_confirmed
2024-08-16T03:40:38.652Z
665ede6c8947302aa2b408d5
/avatars/fc1bcf732a981a78b0afbc8fa0c072cd.svg
李琳
user
lilin1129
66bd603b3f0c44697d3eba39
Guang Liu
claimed_verified
2024-10-28T11:28:31.913Z
632c234f42c386ebd2710434
https://cdn-avatars.huggingface.co/v1/production/uploads/632c234f42c386ebd2710434/HyWRWi063S69JTy_IMjoe.jpeg
Guang Liu
user
ZacLiu
InfinityMATH: A Scalable Instruction Tuning Dataset in Programmatic
  Mathematical Reasoning
Recent advancements in Chain-of-Thoughts (CoT) and Program-of-Thoughts (PoT)
methods have greatly enhanced language models' mathematical reasoning
capabilities, facilitating their integration into instruction tuning datasets
with LLMs. However, existing methods for large-scale dataset creation require
substantial seed data and high computational costs for data synthesis, posing
significant challenges for scalability. We introduce InfinityMATH, a scalable
instruction tuning dataset for programmatic mathematical reasoning. The
construction pipeline emphasizes decoupling numbers from mathematical problems
to synthesize number-independent programs, enabling efficient and flexible
scaling while minimizing dependency on specific numerical values. Fine-tuning
experiments with open-source language and code models, such as Llama2 and
CodeLlama, demonstrate the practical benefits of InfinityMATH. These fine-tuned
models, showed significant relative improvements on both in-domain and
out-of-domain benchmarks, ranging from 184.7% to 514.3% on average.
Additionally, these models exhibited high robustness on the GSM8K+ and MATH+
benchmarks, which are enhanced version of test sets with simply the number
variations. InfinityMATH ensures that models are more versatile and effective
across a broader range of mathematical problems. The data is available at
https://huggingface.co/datasets/flagopen/InfinityMATH.
InfinityMATH is a scalable dataset for programmatic mathematical reasoning that decouples numbers from problems, enhancing model performance and robustness across benchmarks.
Chain-of-Thoughts Program-of-Thoughts mathematical reasoning instruction tuning datasets large-scale dataset creation data synthesis decoupling number-independent programs fine-tuning Llama2 CodeLlama in-domain benchmarks out-of-domain benchmarks GSM8K+ MATH+