Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games

Understand

Large language models (LLMs) have been observed to suddenly exhibit advanced reasoning abilities during reinforcement learning (RL), resembling an ``aha moment'' triggered by simple outcome-based rewards.

• While RL has proven effective in eliciting such breakthroughs in tasks involving mathematics, coding, and vision, it faces significant challenges in multi-scenario games.
• The diversity of game rules, interaction modes, and environmental complexities often leads to policies that perform well in one scenario but fail to generalize to others.
• Simply combining multiple scenarios during training introduces additional challenges, such as training instability and poor performance.