1 citations · 1 across the 6 of their papers we have counts for
9 papers
How RL Reshapes LLM Reasoning: Transferability, Coverage, and Scaling Laws
Ziheng Cheng, Yixiao Huang, Hanlin Zhu +1
Recent studies on reinforcement learning (RL) report seemingly conflicting evidence about large language model (LLM) reasoning. Training on mathematics can improve performance in o…
Periodic Weak Spots: Phase Sensitivity from Chunked KV-Cache Compression
Xingyu Zhu, Pu, Yi +6
Chunked KV-cache compression reduces the memory and attention costs of long-context inference by compressing windows of consecutive tokens into fewer cache entries at a fixed strid…
Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies
Ziheng Cheng, Xin Guo, Huyên Pham +1
This paper develops a model-free reinforcement learning framework for continuous--time extended mean field control problems, where both the dynamics and reward may depend on the jo…
Multi-Mask Diffusion Language Models for Few-Step Generation
Sijin Chen, Yinuo Ren, Heyang Zhao +3
Masked diffusion models (MDMs) are a promising family of language generators, but achieving high-quality few-step generation remains challenging. In MDMs, all forward trajectories…
Data-Efficient Training by Evolved Sampling
Ziheng Cheng, Zhong Li, Jiang Bian
Data selection is designed to accelerate learning with preserved performance. To achieve this, a fundamental thought is to identify informative data samples with significant contri…
Deterministic Policy Gradient for Reinforcement Learning with Continuous Time and State
Ziheng Cheng, Xin Guo, Yufei Zhang
The theory of continuous-time reinforcement learning (RL) has progressed rapidly in recent years. While the ultimate objective of RL is typically to learn deterministic control pol…
OVERT: A Benchmark for Over-Refusal Evaluation on Text-to-Image Models
Ziheng Cheng, Yixiao Huang, Hui Xu +4
Text-to-Image (T2I) models have achieved remarkable success in generating visual content from text inputs. Although multiple safety alignment strategies have been proposed to preve…
Convergence of Distributed Adaptive Optimization with Local Updates
Ziheng Cheng, Margalit Glasgow
We study distributed adaptive algorithms with local updates (intermittent communication). Despite the great empirical success of adaptive methods in distributed training of modern…
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari +5
Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD. Despite this success, theoretically…