The llMS improves all the domains most of the domains, but their performance in the fight against complex financial problems is always a place of an effective investigation. The Interative Development of LLMS conducts a boundary by emergence of generic artificial artificial artificial intelligence (AGI). The Ovaia's O1 series and similar models are like QWQ and Marco-O1 develop complex consult skills “that is thought to” consult “testing”. Financial, models such as Xuauan-Finx1-Preview and Finvo1 has shown the power of lls in the mind-set. At that time, Deepseekr1 adopts a different strategy, depends largely on RL with many categories training to develop consultation and approval. By combining thousands of illegal rl steps with a small dataset to the cold start, depth
In addition to these developmental, the standardized llms strives to adapt to special financial thoughts. Making financial decisions require medical information, including lawrics, economic indicators, and mathematical, while seeking logical thinking, steps by step. Several challenges appear when you send llms to the financial system. First, the deductible financial data includes the integration of information, which results in non-compliance with the complete understanding of the full understanding. Second, the nature of the black box of the llms makes their thinking process difficult to translate, conflict with the regulations of visibility and accountability. Finally, the llms often struggled with financial situations, producing unfaithful effects on a major risk use. This estimated putting important obstacles to their hands in real financial programs, where accurate and attention is very important.
Investigators from Shanghai University of Finance & Economics, Fudan University, and Finntep develop Fin-R1, a special financial viability of the financial discussion. With a compact 7-billion parameter, Fin-R1 reduces the cost of submission when addressing important economic challenges: Separated data, a lack of consultation, and strong control. Training in Fin-R1-Data, high-quality data containing 60,091 cot received in authorized financial information. The two-step training system – the best guide (sft) followed by RL-Fin-R1 improves accuracy and study. Works well in financial decks, exceeding tracking and advising systems.
Studies show a two-class structure of building Fin-R1. Data generation phase includes creating high-quality financial data, Fin-R1 data, using data distillation with deepseek-R1 and filtering using the llm-As-APTE method using the llM-As-APTE method. In the Model Training Phase, Fin-R1 is well organized in QWEN2.5-7b-read using SFT and GRPO) to improve compliance and output. The data set include open data and financial information, refined with difficult filtering. Training includes strengthening and strengthening strengthening, including formal policies and reward prizes to improve accuracy of financial management and standing.
Fin-R1 Thinking skills in financial cases are assessed by funding against several Art-of-the-art models including Deepseek-R1, Fin-SFT, and the MBOK of various buildings. Apart from its compact 7B parameter, Finn-R1 has achieved 75.2 perimeter. It is all the same models and exceeded Deepseek-R1-Distill-Lla-70b with 8.7 points. FIN-R1 is highlighted with the top 76.0 and 85.0, separately, by memorizing financial and benchmarks, TFNS, and funding.
In conclusion, Fin-R1 is a large model of currencies that reflects the funds designed to address the important financial challenges Ai, including divorce data, logic inconsistent, and limited business. It gives Story-of-the-art performance through the two stage training process and RL-on the Fina-R1-Dead Dean Dataset. With the Compact 7B parameter, it reaches 85.0 scores in Conxinqa and 76.0 in Seek, large models. The future work aims to develop the power of the financial multimodal, strengthen compliance with control, and increase the actual international apps, new driving in the Empines.
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Sana Hassan, a contact in MarktechPost with a student of the Dual-degree student in the IIit Madras, loves to use technology and ai to deal with the real challenges of the world. I'm very interested in solving practical problems, brings a new view of ai solution to AI and real solutions.
