BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoning

Researchers trained a compact 1.5B parameter language model to solve beam physics problems using reinforcement learning with verifiable rewards, achieving 66.7% improvement in accuracy. However, the model learned pattern-matching templates rather than true physics reasoning, failing to generalize to topological changes despite mastering the same underlying equations.