Agentic Retrieval and Reinforcement Learned Equation Chains: A Controlled Generation Framework for Complex and Novel Physics Word Problems

In three linesARVRE combines offline reinforcement learning, agentic RAG, and LLMs to generate complex, solvable physics word problems. Stage one builds valid equation chains via temporal-difference learning; stage two converts chains into natural-language questions. Human and automated evaluations show superiority in complexity, novelty, and solvability.

Summary generated by Claude — human-verified