Title: WirelessMathLM: Teaching Mathematical Reasoning for LLMs in Wireless Communications with Reinforcement Learning
Primary area: datasets and benchmarks
Keywords: large language models, mathematical reasoning, wireless communications, reinforcement learning, benchmark datasets

Abstract: 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 \textbf{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 $\approx$100× 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. 
We will release our benchmark, models, and codes to accelerate research in efficient, specialized AI for technical domains.

TL;DR: We train 0.5B-7B models with GRPO on wireless math, achieving 39.5% accuracy (near GPT-4o) with only 7B parameters and unexpectedly gaining 8.4 points on general math benchmarks using WirelessMathBench-XL (4,027 problems).