3 papers
cs.LG2026
Curriculum Learning-Guided Progressive Distillation in Large Language Models
Jincheng Cao, Fanzhi Zeng, Leqi Liu +1
Knowledge distillation is a key technique for transferring the capabilities of large language models (LLMs) into smaller, more efficient student models. Existing distillation appro…
cs.LG2026
A Learnable Wavelet Transformer for Long-Short Equity Trading and Risk-Adjusted Return Optimization
Shuozhe Li, Du Cheng, Leqi Liu
Learning profitable intraday trading policies from financial time series is challenging due to heavy noise, non-stationarity, and strong cross-sectional dependence among related as…
cs.LG2026
CARE-RFT: Confidence-Anchored Reinforcement Finetuning for Reliable Reasoning in Large Language Models
Shuozhe Li, Jincheng Cao, Bodun Hu +3
Reinforcement finetuning (RFT) has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models. However, we identify a critical trade-off: while un…