Sheng Di

The paper proposes a unified, constrained optimization framework using KL divergence and likelihood constraints to achieve effective and principled unlearning in diffusion models.

The paper introduces Straggler-Aware Group Control (SAGC), a dynamic group-size controller that optimizes synchronous on-policy RL training by adapting group size to minimize delays caused by slow rollouts (stragglers), thereby improving wall-clock efficiency and model performance.

This paper systematically studies how soft errors propagate during Large Language Model (LLM) inference using a novel fault-injection framework, providing critical insights and mitigation strategies for improving LLM reliability.