2 papers
cs.LG2026
When Denser Credit Is Not Enough: Evidence-Calibrated Policy Optimization for Long-Horizon LLM Agent Training
Yuanfan Li, Qi Zhou, Wenjing Duan +1
Long-horizon LLM agents require reinforcement learning methods that can assign credit to intermediate decisions under sparse and delayed rewards. Recent group-based methods such as…
cs.CL2026
Measuring Maximum Activations in Open Large Language Models
Luxuan Chen, Han Tian, Xinran Chen +9
The dynamic range of activations is a first-order constraint for low-bit quantization, activation scaling, and stable LLM inference. Prior work characterized outlier features and m…