From the 1 of 12 linked papers with an AI index.
12 papers
Memory for Large Language Models
Sining Zhoubian, Dan Zhang, Evgeny Kharlamov +1
The paper surveys and categorizes the various memory mechanisms used in large language models, proposing a taxonomy based on representation, update dynamics, and persistence to uni…
Can Large Language Models Master Complex Card Games?
Wei Wang, Fuqing Bie, Junzhe Chen +4
Complex games have long been an important benchmark for testing the progress of artificial intelligence algorithms. AlphaGo, AlphaZero, and MuZero have defeated top human players i…
TDRM: Smooth Reward Models with Temporal Difference for LLM RL and Inference
Dan Zhang, Min Cai, Jonathan Light +3
Reward models are central to both reinforcement learning (RL) with language models and inference-time verification. However, existing reward models often lack temporal consistency,…
ReST-RL: Achieving Accurate Code Reasoning of LLMs with Optimized Self-Training and Decoding
Sining Zhoubian, Dan Zhang, Jie Tang
With respect to improving the reasoning accuracy of LLMs, the representative reinforcement learning (RL) method GRPO faces failure due to insignificant reward variance, while verif…
ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think
Tao Feng, Wei Li, Didi Zhu +4
Backpropagation provides a generalized configuration for overcoming catastrophic forgetting. Optimizers such as SGD and Adam are commonly used for weight updates in continual learn…
SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models
Jiale Cheng, Xiao Liu, Cunxiang Wang +7
Instruction-following is a fundamental capability of language models, requiring the model to recognize even the most subtle requirements in the instructions and accurately reflect…