---
title: "AGiR: Mitigating Gift Over-Reliance in Mixed-Motive Games"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.02875v1"
permalink: "https://feed.astralor.com/items/itm_0055af6a678848c46802f6fc"
published: "2026-11-02T10:13:21.000Z"
score: 56
channel: "tech"
tags: ["AI Agent","推理效率","AGiR","Mitigating Gift Over-Reliance","Mixed-Motive Games","MMGs","Our"]
---

# AGiR: Mitigating Gift Over-Reliance in Mixed-Motive Games

## At a glance

In this paper, we consider mixed-motive games (MMGs) which model various real-world multi-agent scenarios where self-interested agents must both cooperate and compete. We identify an issue with existing gifting methods,…

## Key points

- In this paper, we consider mixed-motive games (MMGs) which model various real-world multi-agent scenarios where self-interested agents must both cooperate and compete.
- We identify an issue with existing gifting methods, where agents become overly dependent on incoming gifts and fail to learn reward-yielding behaviors, a phenomenon we call gift over-reliance.
- To address this, we propose a refusal-augmented gifting framework that balances cooperation and competition in MMGs.

## Full text

In this paper, we consider mixed-motive games (MMGs) which model various real-world multi-agent scenarios where self-interested agents must both cooperate and compete. We identify an issue with existing gifting methods, where agents become overly dependent on incoming gifts and fail to learn reward-yielding behaviors, a phenomenon we call gift over-reliance. To address this, we propose a refusal-augmented gifting framework that balances cooperation and competition in MMGs. Our approach introduces an adaptive refusal mechanism that allows agents to selectively reject a portion of received gifts from their policy-update signals, thereby mitigating gift over-reliance and preventing the agents from being exploited by other agents. This mechanism is gifting-method-agnostic, serving as a plug-in for existing schemes while enabling decentralized learning. We demonstrate that across diverse MMG environments and gifting schemes, our mechanism consistently reduces gift over-reliance and prevents lock-in to sacrificing policies, improving $α$-fairness as a result, without necessitating major changes to existing training pipelines.

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