Qin-Wen Luo

Small language models can now master complex tool use by learning from teachers without inheriting their biases or forgetting their own exploration gains.

Uncertainty-aware querying can significantly stabilize policy updates in offline reinforcement learning, leading to improved performance without additional environment interaction.

LLM reasoning can be compressed without sacrificing accuracy by selectively encouraging exploration only on difficult questions, preventing premature entropy collapse.