Guangming Yao

Most self-distillation methods for agentic RL fail to consistently outperform a strong baseline, and impressive final scores often hide unstable training dynamics and substantial computational overhead.

Streaming video MLLMs perform far better when historical context is actively internalized into evolving latent tokens rather than queried as passive external visual buffers.