3 citations · 7 across the 24 of their papers we have counts for
9 papers · 1 filter
Safin-1: Safety from Within through Memory-Native State Evolution
Ming Zhang, Kaisen Yang, Shu Yu +15
Long-horizon complex tasks require foundation models to accumulate information, maintain internal states, and adapt over extended interactions. Safety should be an intrinsic proper…
MARCH: Scaling Recurrent Memory with Content-Routed State Anchors
Ming Zhang, Kaisen Yang, Shu Yu +6
Transformers owe much of their strong long-context retrieval capability to a token-level memory that grows with context length. This flexibility, however, incurs a quadratic comput…
Post-Trained MoE Can Skip Half Experts via Self-Distillation
Xingtai Lv, Li Sheng, Kaiyan Zhang +12
Mixture-of-Experts (MoE) scales language models efficiently through sparse expert activation, and its dynamic variant further reduces computation by adjusting the activated experts…
\texttt{RAI}: Towards Resistant and Resilient AI in an Evolving World
Youbang Sun, Xiang Wang, Jie Fu +2
In this position paper, we address the persistent gap between rapidly growing AI capabilities and lagging safety progress. Existing paradigms divide into ``Make AI Safe'', which ap…
FlowRL: Matching Reward Distributions for LLM Reasoning
Xuekai Zhu, Daixuan Cheng, Dinghuai Zhang +20
We propose FlowRL: matching the full reward distribution via flow balancing instead of maximizing rewards in large language model (LLM) reinforcement learning (RL). Recent advanced…
Towards a Unified View of Large Language Model Post-Training
Xingtai Lv, Yuxin Zuo, Youbang Sun +8
Two major sources of training data exist for post-training modern language models: online (model-generated rollouts) data, and offline (human or other-model demonstrations) data. T…
The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models
Ganqu Cui, Yuchen Zhang, Jiacheng Chen +14
This paper aims to overcome a major obstacle in scaling RL for reasoning with LLMs, namely the collapse of policy entropy. Such phenomenon is consistently observed across vast RL r…
Deep Unfolding with Kernel-based Quantization in MIMO Detection
Zeyi Ren, Jingreng Lei, Yichen Jin +5
The development of edge computing places critical demands on energy-efficient model deployment for multiple-input multiple-output (MIMO) detection tasks. Deploying deep unfolding m…
Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models
Xingtai Lv, Youbang Sun, Kaiyan Zhang +8
State Space Models (SSMs) have emerged as a promising alternative to the popular transformer-based models and have been increasingly gaining attention. Compared to transformers, SS…