10 citations · 19 across the 17 of their papers we have counts for
4 papers · 1 filter
AutoScientist-Quant: Self-Evolving Coding Agents for Automatic Research in Quantitative Investment
Zongqian Li, Yaoyiran Li, Yaohui Guo +3
Large language model agents can discover alphas, yet current methods have three weaknesses. The search cannot adapt during the run, automation usually ends at alpha generation whil…
Flexi-LoRA with Input-Adaptive Ranks: Efficient Finetuning for Speech and Reasoning Tasks
Zongqian Li, Yixuan Su, Han Zhou +2
Parameter-efficient fine-tuning methods like Low-Rank Adaptation (LoRA) have become essential for deploying large language models, yet their static parameter allocation remains sub…
Scaling Data Difficulty: Improving Coding Models via Reinforcement Learning on Fresh and Challenging Problems
Zongqian Li, Tengchao Lv, Shaohan Huang +8
Training next-generation code generation models requires high-quality datasets, yet existing datasets face difficulty imbalance, format inconsistency, and data quality problems. We…
Breaking Training Bottlenecks: Effective and Stable Reinforcement Learning for Coding Models
Zongqian Li, Shaohan Huang, Zewen Chi +5
Modern code generation models exhibit longer outputs, accelerated capability growth, and changed training dynamics, rendering traditional training methodologies, algorithms, and da…