Hongbang Yuan

Rather than relying on scarce human demonstrations to warm up reasoning agents, dynamically scaffolding the environment's feedback allows LLMs to autonomously crack sparse-reward, long-horizon tasks and bake exploration behaviors directly into their weights.

Simply adding more multimodal environments can hinder agent performance, but targeted diversity and structured difficulty can transform training outcomes.

SFT leads to task conflicts that can cripple multi-task learning, while RL's variance-limited updates enable seamless task coexistence.