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CORAL: An LLM-Native Harness for Production Recommender Systems
Muhammad Rafay Azhar, Yuhang Zhou, Gilbert Jiang +7
Production recommender systems shape what billions of people see, and sustaining their performance requires continual optimization: as content, user behavior, and upstream models s…
SAGE-OPD: Selective Agent-Guided Intervention for Multi-Turn On-Policy Distillation
Yuhang Zhou, Lizhu Zhang, Yifan Wu +5
On-policy distillation (OPD) improves student models by training them on trajectories induced by their own policy, making it a promising approach for mitigating exposure bias in ag…
Agentic Recommender System with Hierarchical Belief-State Memory
Xiang Shen, Yuhang Zhou, Yifan Wu +8
Memory-augmented LLM agents have advanced personalized recommendation, yet existing approaches universally adopt flat memory representations that conflate ephemeral signals with st…
Synthetic Sandbox for Training Machine Learning Engineering Agents
Yuhang Zhou, Lizhu Zhang, Yifan Wu +4
As large language model agents advance beyond software engineering (SWE) tasks toward machine learning engineering (MLE), verifying agent behavior becomes orders of magnitude more…
LLM-Driven Reasoning for Constraint-Aware Feature Selection in Industrial Systems
Yuhang Zhou, Zhuokai Zhao, Ke Li +14
Feature selection is a crucial step in large-scale industrial machine learning systems, directly affecting model accuracy, efficiency, and maintainability. Traditional feature sele…
TARo: Token-level Adaptive Routing for LLM Test-time Alignment
Arushi Rai, Qiang Zhang, Hanqing Zeng +5
Large language models (LLMs) exhibit strong reasoning capabilities but typically require expensive post-training to reach high performance. Recent test-time alignment methods offer…
Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding
Yuhang Zhou, Mingrui Zhang, Ke Li +12
Understanding and reasoning over tables is a critical capability for many real-world applications. Large language models (LLMs) have shown promise on this task, but current approac…
Let it Calm: Exploratory Annealed Decoding for Verifiable Reinforcement Learning
Chenghao Yang, Lin Gui, Chenxiao Yang +3
Reinforcement learning with verifiable rewards (RLVR) is a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs), yet its success hinges on eff…
GEM: Empowering LLM for both Embedding Generation and Language Understanding
Caojin Zhang, Qiang Zhang, Ke Li +6
Large decoder-only language models (LLMs) have achieved remarkable success in generation and reasoning tasks, where they generate text responses given instructions. However, many a…
S'MoRE: Structural Mixture of Residual Experts for Parameter-Efficient LLM Fine-tuning
Hanqing Zeng, Yinglong Xia, Zhuokai Zhao +7
Fine-tuning pre-trained large language models (LLMs) presents a dual challenge of balancing parameter efficiency and model capacity. Existing methods like low-rank adaptations (LoR…