Long-context financial document understanding and reasoning pose significant challenges for small language models (SLMs). In this paper, we scale the financial long-context reasoning capability of SLMs through reinforcement learning. Specifically, we propose an efficient curriculum reinforcement learning recipe that features staged training across context lengths and difficulty-aware data sampling. To support our curriculum recipe, we curate Fin-Ladder, a difficulty-graded financial corpus spanning diverse datasets and context lengths. We apply the proposed corpus and recipe to train Financial Long-Context Reasoner (FinLCR), a 4-billion parameter model with enhanced financial reasoning capabilities. Experiments demonstrate that our curriculum learning recipe yields superior performance on financial numerical reasoning benchmarks.