1 citations · 1 across the 5 of their papers we have counts for
6 papers
MInTRL: Off-policy Intervention can boost On-policy RL
Mingyu Chen, Yefan Tao, Gerald Friedland +2
Reinforcement learning with verifiable rewards is typically performed on-policy, keeping training data close to the current policy but limiting learning to trajectories that the po…
Mitra-v2 Technical Report
Yefan Tao, Xiyuan Zhang, Xinyi Liu +13
We introduce Mitra-v2, a tabular foundation model that delivers state-of-the-art performance on real-world classification and regression problems, from credit-risk scoring and clin…
When Is Noise Response Universal? Tokenization as the Hidden Variable in Language Models
Yefan Tao, Gerald Friedland, Luyang Kong
The performance of textual neural models often degrades when their inputs are corrupted by noise such as typos, OCR errors, or dropped words. We study the degradation rate across n…
Reflection or Re-Generation? Why LLM Revision Fails Where Human Revision Succeeds
Yefan Tao, Gerald Friedland, Madhusudhanan Chandrasekaran +1
Reflection, the ability to revisit and revise prior reasoning, is central to how humans improve their answers. Large language models (LLMs) are increasingly prompted to "reflect,"…
Declarative Data Pipeline for Large Scale ML Services
Yunzhao Yang, Runhui Wang, Xuanqing Liu +14
Modern distributed data processing systems struggle to balance performance, maintainability, and developer productivity when integrating machine learning at scale. These challenges…
Neural Locality Sensitive Hashing for Entity Blocking
Runhui Wang, Luyang Kong, Yefan Tao +6
Locality-sensitive hashing (LSH) is a fundamental algorithmic technique widely employed in large-scale data processing applications, such as nearest-neighbor search, entity resolut…