most citedReinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2025

RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models

Hua Zong, Qingtao Zeng, Zhengxiong Zhou +31

In this paper, we propose RecIS, a unified Sparse-Dense training framework designed to achieve two primary goals: 1. Unified Framework To create a Unified sparse-dense training fra…

cs.CV2025

Four Eyes Are Better Than Two: Harnessing the Collaborative Potential of Large Models via Differentiated Thinking and Complementary Ensembles

Jun Xie, Xiongjun Guan, Yingjian Zhu +5

In this paper, we present the runner-up solution for the Ego4D EgoSchema Challenge at CVPR 2025 (Confirmed on May 20, 2025). Inspired by the success of large models, we evaluate an…

cs.LG20251 cited

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Weixun Wang, Shaopan Xiong, Gengru Chen +38

We introduce ROLL, an efficient, scalable, and user-friendly library designed for Reinforcement Learning Optimization for Large-scale Learning. ROLL caters to three primary user gr…

cs.CL2025

JTCSE: Joint Tensor-Modulus Constraints and Cross-Attention for Unsupervised Contrastive Learning of Sentence Embeddings

Tianyu Zong, Hongzhu Yi, Bingkang Shi +2

Unsupervised contrastive learning has become a hot research topic in natural language processing. Existing works usually aim at constraining the orientation distribution of the rep…

cs.CV2025

LCGC: Learning from Consistency Gradient Conflicting for Class-Imbalanced Semi-Supervised Debiasing

Weiwei Xing, Yue Cheng, Hongzhu Yi +5

Classifiers often learn to be biased corresponding to the class-imbalanced dataset, especially under the semi-supervised learning (SSL) set. While previous work tries to appropriat…