2 papers
cs.LG2026
RAP: Runtime Adaptive Pruning for LLM Inference
Huanrong Liu, Chunlin Tian, Xuyang Wei +2
Large language models (LLMs) excel at language understanding and generation, but their enormous computational and memory requirements hinder deployment. Compression offers a potent…
cs.LG2025
D2C-HRHR: Discrete Actions with Double Distributional Critics for High-Risk-High-Return Tasks
Jundong Zhang, Yuhui Situ, Fanji Zhang +2
Tasks involving high-risk-high-return (HRHR) actions, such as obstacle crossing, often exhibit multimodal action distributions and stochastic returns. Most reinforcement learning (…