12 papers
Dynamic Linear Attention
Xin Wang, Hui Shen, Boyuan Zheng +7
The scalability of Large Language Models (LLMs) to long contexts is fundamentally constrained by the quadratic complexity of standard attention, motivating the adoption of linear a…
How Does Reasoning Flow? Tracing Attention-Induced Information Flow for Targeted RL in LLMs
Zhichen Dong, Yang Li, Yuhan Sun +9
Token-level credit assignment remains a key obstacle for reinforcement learning (RL) in large language models (LLMs), where RL recipes typically treat all tokens equally, failing t…
CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling
Runsong Zhao, Shilei Liu, Jiwei Tang +8
The quadratic complexity and indefinitely growing key-value (KV) cache of standard Transformers pose a major barrier to long-context processing. To overcome this, we introduce the…
PoC: Performance-oriented Context Compression for Large Language Models via Performance Prediction
Runsong Zhao, Shilei Liu, Jiwei Tang +8
While context compression can mitigate the growing inference costs of Large Language Models (LLMs) by shortening contexts, existing methods that specify a target compression ratio…
COMI: Coarse-to-fine Context Compression via Marginal Information Gain
Jiwei Tang, Shilei Liu, Zhicheng Zhang +4
Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse tasks. However, their deployment in long context scenarios remains hindered by computational…
Read As Human: Compressing Context via Parallelizable Close Reading and Skimming
Jiwei Tang, Shilei Liu, Zhicheng Zhang +9
Large Language Models (LLMs) demonstrate exceptional capability across diverse tasks. However, their deployment in long-context scenarios is hindered by two challenges: computation…