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.

As large language models (LLMs) continue to advance, significant effort is spent on manually crafting prompts to unlock their full potential. While existing prompt optimization methods automate this process, they often underperform due to their reliance on learning exclusively from incorrect samples. We propose the Learning from Contrastive Prompts (LCP) framework, which leverages contrastive prompts to distinguish between high- and low-performing cases. By identifying and amplifying the differences that make prompts effective, LCP systematically extracts principles underlying successful prompt design. On the Big-Bench Hard benchmark, LCP achieves an 87.5% win rate on Claude-3-Sonnet and 75.7% on Claude-4-Sonnet. Experiments on DeepSeek-R1 (88.2% win rate) and SuperGLUE further confirm that LCP generalizes across both proprietary and open-source models and diverse NLU benchmarks.The framework offers a principled and scalable foundation for automated prompt engineering, reducing manual intervention in adapting LLMs to diverse applications.

Large language models (LLMs) have demonstrated remarkable open-domain capabilities. LLMs tailored for a domain are typically trained entirely on domain corpus to excel at handling domain-specific tasks. In this work, we explore an alternative strategy of continual pre-training as a means to develop domain-specific LLMs over an existing open-domain LLM. We introduce FinPythia-6.9B, developed through domain-adaptive continual pre-training on the financial domain.Continual pre-trained FinPythia showcases consistent improvements on financial tasks over the original foundational model. We further explore simple but effective data selection strategies for continual pre-training. Our data selection strategies outperform vanilla continual pre-training’s performance with just 10% of corpus size and cost, without any degradation on open-domain standard tasks. Our work proposes an alternative solution to building domain-specific LLMs cost-effectively.