@inproceedings{qiu-etal-2026-finlcr,
title = "{F}in{LCR}: Scaling Financial Long-Context Reasoning via Curriculum Reinforcement Learning",
author = "Qiu, Wenjie and
Xie, Yong and
Aggarwal, Karan and
Ahmad, Aitzaz and
Lau, Stephen",
editor = "Huang, Jimin and
Lee, Yongjae and
Ananiadou, Sophia and
Chen, Hsin-Hsi and
Takamura, Hiroya and
Chen, Chung-Chi",
booktitle = "Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.finnlp-1.3/",
pages = "15--25",
ISBN = "979-8-89176-466-8",
abstract = "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."
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%0 Conference Proceedings
%T FinLCR: Scaling Financial Long-Context Reasoning via Curriculum Reinforcement Learning
%A Qiu, Wenjie
%A Xie, Yong
%A Aggarwal, Karan
%A Ahmad, Aitzaz
%A Lau, Stephen
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F qiu-etal-2026-finlcr
%X 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.
%U https://aclanthology.org/2026.finnlp-1.3/
%P 15-25
Markdown (Informal)
[FinLCR: Scaling Financial Long-Context Reasoning via Curriculum Reinforcement Learning](https://aclanthology.org/2026.finnlp-1.3/) (Qiu et al., FinNLP 2026)
ACL