WirelessMathLM: Teaching Mathematical Reasoning for LLMs in Wireless
  Communications with Reinforcement Learning
Large language models (LLMs) excel at general mathematical reasoning but fail
catastrophically on specialized technical mathematics. In wireless
communications, where problems require precise manipulation of
information-theoretic bounds, optimization constraints, and signal processing
formulations, even state-of-the-art models struggle to achieve competent
performance. We present WirelessMathLM, demonstrating that compact models
(0.5B-7B parameters) can match or exceed much larger models through
domain-specific reinforcement learning with verifiable rewards. Our key insight
is that wireless mathematics problems possess a unique property--verifiable
correctness--that enables effective reinforcement learning without human
feedback. We construct WirelessMathBench-XL, a comprehensive benchmark of 4,027
problems from 970 papers. Using Group Relative Policy Optimization (GRPO) with
binary verification rewards, we train models directly from base checkpoints
without supervised warm-start. Our 7B model achieves 39.5% accuracy on
WirelessMathBench-XL, approaching GPT-4o (40.4%) while using about 100 times
fewer parameters than DeepSeek-R1 (671B, 57.4%). Remarkably, GRPO training
nearly doubles performance across all model scales (0.5B +11%, 3B +103%, 7B
+81%), with positive transfer to general mathematics benchmarks--our models
gain +8.4 points on average across MATH, Minerva-Math, OlympiadBench, AMC, and
AIME without any training on these tasks.
68db8c98d2bf1f4b15ec7860
Xin Li
claimed_verified
2025-09-30T10:17:55.697Z
64a948d9723beceb2f13f5eb
https://cdn-avatars.huggingface.co/v1/production/uploads/64a948d9723beceb2f13f5eb/UQMO7LczJpabtbcoNt1zQ.jpeg
XinLi
user
XINLI1997
68db8c98d2bf1f4b15ec7861
Mengbing Liu
68db8c98d2bf1f4b15ec7862
Yiyang Zhu
68db8c98d2bf1f4b15ec7863
Wenhe Zhang
68db8c98d2bf1f4b15ec7864
Li Wei
68db8c98d2bf1f4b15ec7865
Jiancheng An
68db8c98d2bf1f4b15ec7866
Chau Yuen
WirelessMathLM: Teaching Mathematical Reasoning for LLMs in Wireless
  Communications with Reinforcement Learning
Large language models (LLMs) excel at general mathematical reasoning but fail
catastrophically on specialized technical mathematics. In wireless
communications, where problems require precise manipulation of
information-theoretic bounds, optimization constraints, and signal processing
formulations, even state-of-the-art models struggle to achieve competent
performance. We present WirelessMathLM, demonstrating that compact models
(0.5B-7B parameters) can match or exceed much larger models through
domain-specific reinforcement learning with verifiable rewards. Our key insight
is that wireless mathematics problems possess a unique property--verifiable
correctness--that enables effective reinforcement learning without human
feedback. We construct WirelessMathBench-XL, a comprehensive benchmark of 4,027
problems from 970 papers. Using Group Relative Policy Optimization (GRPO) with
binary verification rewards, we train models directly from base checkpoints
without supervised warm-start. Our 7B model achieves 39.5% accuracy on
WirelessMathBench-XL, approaching GPT-4o (40.4%) while using about 100 times
fewer parameters than DeepSeek-R1 (671B, 57.4%). Remarkably, GRPO training
nearly doubles performance across all model scales (0.5B +11%, 3B +103%, 7B
+81%), with positive transfer to general mathematics benchmarks--our models
gain +8.4 points on average across MATH, Minerva-Math, OlympiadBench, AMC, and
AIME without any training on these tasks.
WirelessMathLM, a compact model trained with domain-specific reinforcement learning, achieves high accuracy on wireless mathematics problems and transfers well to general mathematics benchmarks.
Large language models wireless communications information-theoretic bounds optimization constraints signal processing domain-specific reinforcement learning verifiable rewards WirelessMathLM WirelessMathBench-XL Group Relative Policy Optimization GRPO binary verification rewards DeepSeek-R1 MATH Minerva-Math OlympiadBench AMC AIME