2 citations · 2 across the 8 of their papers we have counts for
9 papers
Extreme Region Policy Distillation
Changyu Chen, Xiting Wang, Rui Yan
Reinforcement learning for large language models faces a fundamental trade-off between sample efficiency and asymptotic performance: strictly on-policy methods discard trajectories…
Controlled LLM Training on Spectral Sphere
Tian Xie, Haoming Luo, Haoyu Tang +9
Scaling large models requires optimization strategies that ensure rapid convergence grounded in stability. Maximal Update Parametrization (P) provides a theoretical s…
Making Every Head Count: Sparse Attention Without the Speed-Performance Trade-off
Mingkuan Zhao, Wentao Hu, Jiayin Wang +5
The design of Large Language Models (LLMs) has long been hampered by a fundamental conflict within their core attention mechanism: its remarkable expressivity is built upon a compu…
SUDER: Self-Improving Unified Large Multimodal Models for Understanding and Generation with Dual Self-Rewards
Jixiang Hong, Yiran Zhang, Guanzhong Wang +3
Building upon large language models (LLMs), recent large multimodal models (LMMs) unify cross-model understanding and generation into a single framework. However, LMMs still strugg…
O1 Embedder: Let Retrievers Think Before Action
Ruiran Yan, Zheng Liu, Defu Lian
The growing power of large language models (LLMs) has revolutionized how people access and utilize information. Notably, the LLMs excel at performing fine-grained data representati…
Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models
Changyu Chen, Xiting Wang, Ting-En Lin +6
In reasoning tasks, even a minor error can cascade into inaccurate results, leading to suboptimal performance of large language models in such domains. Earlier fine-tuning approach…
Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use
Yuhan Chen, Ang Lv, Ting-En Lin +5
In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language models (LLMs) significantly affects their performance in tasks demandi…
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment
Jixiang Hong, Quan Tu, Changyu Chen +3
Language models trained on large-scale corpus often generate content that is harmful, toxic, or contrary to human preferences, making their alignment with human values a critical c…
Semi-Offline Reinforcement Learning for Optimized Text Generation
Changyu Chen, Xiting Wang, Yiqiao Jin +5
In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline. Online methods explore the environment at significant time co…