Bei Yu

Scaling large language models (LLMs) improves performance but significantly increases inference costs, with feed-forward networks (FFNs) consuming the majority of computational resources. While Mixture-of-Experts (MoE) architectures can reduce this cost through sparse activation, restructuring ex...

Reinforcement Learning (RL) with sparse outcome rewards suffers from inefficient credit assignment in complex LLM reasoning tasks. While utilizing stronger LLMs as teachers to derive dense token-level supervision offers a cost-effective alternative to proprietary reward models, it relies on the f...

Hallucination detection remains a significant challenge for large language models. Existing agentic applications rely on LLMs to self-assess the factuality of their outputs using single-step “LLM-as-a-judge” prompts. However, even when equipped with ground truth information, current LLMs still fa...