SignalReasoner: Assessing the Upper Bound of 3B Models for Signal Mathematical Reasoning
The paper titled "SignalReasoner" evaluates the upper limit of 3B models in the realm of signal mathematical reasoning, specifically targeting graduate-level challenges. Published as arXiv:2608.17301, it employs Qwen2.5-3B-Base as the foundational model and utilizes the WirelessMATHBench-XL benchmark. The research investigates two training strategies: direct reinforcement learning (RL) and supervised fine-tuning (SFT) followed by RL. Additionally, it assesses GRPO, GSPO, and Geometric-Mean Policy Optimization. This study highlights a significant gap in the exploration of RL applications within signal processing.
Key facts
- The paper is arXiv:2608.17301.
- It assesses the upper bound of 3B models for signal mathematical reasoning.
- It uses Qwen2.5-3B-Base as the base model.
- It focuses on graduate-level signal mathematical problems.
- The benchmark is WirelessMATHBench-XL.
- Two training paradigms are examined: direct RL and SFT followed by RL.
- It benchmarks GRPO, GSPO, and Geometric-Mean Policy Optimization.
- The study addresses a gap: RL for signal processing remains under-explored.
Entities
Institutions
- arXiv