5 citations · 7 across the 6 of their papers we have counts for
8 papers
CERO: Where and When to Allocate Rollouts for RL Post-Training
Yiming Zong, Yige Wang, Xing Hu +2
Adaptive rollout methods for group-relative reinforcement learning typically allocate a fixed per-update budget across prompts. We instead study how to coordinate a finite rollout…
Corruption-Robust Sparse Linear Contextual Bandits with Knapsack Constraints
Yige Wang, Hanyang Li, Yiming Zong +2
We study sparse linear contextual bandits with knapsack constraints under joint reward and consumption corruption. Consumption corruption creates a challenge beyond corrupted rewar…
Learning to Allocate Incentives for Incentivized Advertising via Offline Model-Based Reinforcement Learning
Zilin Zhao, Han Yang, Tianpei Yang +7
Complete your ad view and grab a 5-cent bonus! In incentivized advertising, a platform promises users a bonus before observing downstream ad revenue, encouraging them to click and…
Cross-Epoch Adaptive Rollout Optimization for RL Post-Training
Yiming Zong, Yige Wang, Jiashuo Jiang
LLM post-training often relies on reinforcement learning methods that sample multiple rollouts per prompt, yet most existing approaches use a fixed rollout budget for every prompt,…
Online Semi-infinite Linear Programming: Efficient Algorithms via Function Approximation
Yiming Zong, Jiashuo Jiang
We consider the dynamic resource allocation problem where the decision space is finite-dimensional, yet the solution must satisfy a large or even infinite number of constraints rev…
Adaptive Resolving Methods for Markov Decision Processes with Function Approximations
Jiashuo Jiang, Yinyu Ye, Yiming Zong
Learning the optimal policy for Markov decision process problems (MDPs) from samples is a fundamental problem in online and data-driven decision-making. Function approximations are…
ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code
Xiangru Tang, Yuliang Liu, Zefan Cai +21
Despite Large Language Models (LLMs) like GPT-4 achieving impressive results in function-level code generation, they struggle with repository-scale code understanding (e.g., coming…
Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data?
Xiangru Tang, Yiming Zong, Jason Phang +4
Despite the remarkable capabilities of Large Language Models (LLMs) like GPT-4, producing complex, structured tabular data remains challenging. Our study assesses LLMs' proficiency…