Researchers have fine-tuned a large language model, called Qwen2.5-3B-Base, to improve its mathematical reasoning capabilities in signal processing problems. They used two training paradigms: direct reinforcement learning with verifiable rewards and supervised fine-tuning followed by domain-specific reinforcement learning. The team tested three optimization algorithms - Group Relative Policy Optimization (GRPO), Group Sequence Policy Optimization (GSPO), and Geometric-Mean Policy Optimization (GMPO) - to see which one works best for signal reasoning tasks. Their results show that the model can achieve an overall accuracy of 39.12%, a significant improvement over the untrained model. --- Why it matters: This work matters because it explores how large language models can be fine-tuned to tackle complex mathematical problems in signal processing, which is crucial for applications like wireless communication systems and data analysis. Source: https://arxiv.org/abs/2608.17301