MindGames Arena Generalization Track: In2AI Solution with Delayed Per-Step Reward Attribution 事件

MindGames Arena Generalization Track: In2AI Solution with Delayed Per-Step Reward Attribution arXiv:2606.00017v1 Announce Type: cross Abstract: Training language model agents for multi-agent strategic interaction presents a core difficulty: the quality of any action may depend on future events that never materialize, on moves that violate game rules, or on decisions made by other players. Standard reinforcement learning assumes that rewards can be assigned at each step, but this assumption fail