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cs.CL2025
RETuning: Upgrading Inference-Time Scaling for Stock Movement Prediction with Large Language Models
Xueyuan Lin, Cehao Yang, Ye Ma +7
Recently, large language models (LLMs) have demonstrated outstanding reasoning capabilities on mathematical and coding tasks. However, their application to financial tasks-especial…
cs.CL2025
CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning
Wenqiao Zhu, Ji Liu, Rongjuncheng Zhang +2
Reasoning capability plays a significantly critical role in the the broad applications of Large Language Models (LLMs). To enhance the reasoning performance of LLMs, diverse Reinfo…