AI Research
MindGames Arena Generalization Track: In2AI Solution with Delayed Per-Step Reward Attribution
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 ...
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ArXiv CS.AI
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ArXiv CS.AI
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This research tackles a fundamental challenge in multi-agent AI by developing a novel reward attribution method that handles delayed and entangled outcomes. This breakthrough allows for more stable and efficient training, leading to a smaller, open-source model outperforming larger proprietary systems.
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