6 citations · 8 across the 10 of their papers we have counts for
12 papers
From Valid to Useful: Post-Verification Acquisition for Recursive Self-Improving Recommendation
Tonmoy Hasan, Taylor Foust, Shao Tang +6
Sequential recommenders can generate synthetic interaction sequences and retrain on the augmented corpus in a recursive self-improvement loop. To limit error accumulation, current…
Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework
Aman Gupta, Kevin Rossell, Edesio Alcobaça +8
The rapid rise in LLM capabilities has made AI agents increasingly viable across a broad range of tasks. Among the most promising applications is building production-ready customer…
Bayesian Preference Learning for Test-Time Steerable Reward Models
Jiwoo Hong, Shao Tang, Zhipeng Wang
Reward models are central to aligning language models with human preferences via reinforcement learning (RL). As RL is increasingly applied to settings such as verifiable rewards a…
RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning
Deyi Ji, Yuekui Yang, Liqun Liu +7
Advertising (Ad) is a cornerstone of the digital economy, yet the moderation of video advertisements remains a significant challenge due to their complexity and the need for precis…
Effective Quantization of Muon Optimizer States
Aman Gupta, Rafael Celente, Abhishek Shivanna +7
The Muon optimizer, based on matrix orthogonalization, has recently shown faster convergence and better computational efficiency over AdamW in LLM pre-training. However, the memory…
On the Robustness of Reward Models for Language Model Alignment
Jiwoo Hong, Noah Lee, Eunki Kim +5
The Bradley-Terry (BT) model is widely practiced in reward modeling for reinforcement learning with human feedback (RLHF). Despite its effectiveness, reward models (RMs) trained wi…
LiDDA: Data Driven Attribution at LinkedIn
John Bencina, Erkut Aykutlug, Yue Chen +4
Data Driven Attribution, which assigns conversion credits to marketing interactions based on causal patterns learned from data, is the foundation of modern marketing intelligence a…
Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems
Kayhan Behdin, Ata Fatahibaarzi, Qingquan Song +17
Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications, from search and recommendation systems to generative tasks. Al…
LLM Query Scheduling with Prefix Reuse and Latency Constraints
Gregory Dexter, Shao Tang, Ata Fatahi Baarzi +3
The efficient deployment of large language models (LLMs) in online settings requires optimizing inference performance under stringent latency constraints, particularly the time-to-…
From Features to Transformers: Redefining Ranking for Scalable Impact
Fedor Borisyuk, Lars Hertel, Ganesh Parameswaran +14
We present LiGR, a large-scale ranking framework developed at LinkedIn that brings state-of-the-art transformer-based modeling architectures into production. We introduce a modifie…
AlphaPO: Reward Shape Matters for LLM Alignment
Aman Gupta, Shao Tang, Qingquan Song +10
Reinforcement Learning with Human Feedback (RLHF) and its variants have made huge strides toward the effective alignment of large language models (LLMs) to follow instructions and…
Liger Kernel: Efficient Triton Kernels for LLM Training
Pin-Lun Hsu, Yun Dai, Vignesh Kothapalli +7
Training Large Language Models (LLMs) efficiently at scale presents a formidable challenge, driven by their ever-increasing computational demands and the need for enhanced performa…