2 citations · 4 across the 9 of their papers we have counts for
9 papers
Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?
Wanyi Chen, Xiao Yang, Xu Yang +7
We introduce Agent2 RL-Bench, a compact diagnostic benchmark for evaluating agentic RL post-training, which tests whether LLM agents can autonomously design, implement, debug, and…
Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search
Yifei Zhang, Xu Yang, Xiao Yang +8
LLM-based agents for machine learning engineering (MLE) predominantly rely on tree search, a form of gradient-free optimization that uses scalar validation scores to rank candidate…
FT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents
Qizheng Li, Yifei Zhang, Xiao Yang +4
Fine-tuning large language models for vertical domains remains labor-intensive, requiring practitioners to curate data, configure training, and iteratively diagnose model behavior.…
R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
Yuante Li, Xu Yang, Xiao Yang +4
Financial markets pose fundamental challenges for asset return prediction due to their high dimensionality, non-stationarity, and persistent volatility. Despite advances in large l…
R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science
Xu Yang, Xiao Yang, Shikai Fang +13
Recent advances in AI and ML have transformed data science, yet increasing complexity and expertise requirements continue to hinder progress. Although crowd-sourcing platforms alle…
BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning
Jianming Pan, Zeqi Ye, Xiao Yang +4
Data-driven decision-making processes increasingly utilize end-to-end learnable deep neural networks to render final decisions. Sometimes, the output of the forward functions in ce…
Collaborative Evolving Strategy for Automatic Data-Centric Development
Xu Yang, Haotian Chen, Wenjun Feng +7
Artificial Intelligence (AI) significantly influences many fields, largely thanks to the vast amounts of high-quality data for machine learning models. The emphasis is now on a dat…
Towards Data-Centric Automatic R&D
Haotian Chen, Xinjie Shen, Zeqi Ye +6
The progress of humanity is driven by those successful discoveries accompanied by countless failed experiments. Researchers often seek the potential research directions by reading…
Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle
Xu Yang, Xiao Yang, Weiqing Liu +4
In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detecti…