Setting appropriate prices for second-hand products remains challenging for inexperienced individual sellers on Consumer-to-Consumer (C2C) e-commerce platforms. Existing regression-based approaches rely on static product–price mappings, making it difficult to capture subtle condition differences expressed in colloquial product descriptions. Inspired by recent breakthroughs in Large Language Models (LLMs), we present LLP, a retrieval-then-generation framework that generates price suggestions based on dynamically retrieved comparable products. LLP employs bidirectional reasoning to generate high-quality rationales as supervision, and subsequently applies retrieval-aware policy optimization to further improve both reasoning faithfulness and pricing accuracy. At inference time, LLP employs entropy-guided filtering to discard low-confidence price predictions. Extensive evaluations demonstrate the effectiveness of our method. It raises the static adoption rate (SAR) from 40% to 72%, while increasing GMV by 0.7% and CTCVR by 1.4%. LLP is now fully deployed in Xianyu, serving millions of individual sellers daily.