Xun Wu

Experiential Learning outperforms traditional reinforcement learning by providing richer feedback, leading to better generalization and reduced reward hacking in LLM training.

Language models can learn directly from real-world user interactions, boosting performance without human annotations or simulated environments.

1.58-bit LLMs are surprisingly more resilient to sparsity than their full-precision counterparts, opening new avenues for extreme compression.