Implement techniques from GameTalk: Training LLMs for Strategic Conversation. Strategic decision-making in multi-agent settings is a key challenge for large language models (LLMs), particularly when coordination and negotiation must unfold over extended conversations
Scanned 9/9/2026
Install to Claude Code
npx -y skills add ADu2021/skillXiv --skill gametalk-training-llms-for-strategic-conversation --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: gametalk-training-llms-for-strategic-conversation
title: "GameTalk: Training LLMs for Strategic Conversation"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.16276"
keywords: ["agent", "model", "training"]
description: "Implement techniques from GameTalk: Training LLMs for Strategic Conversation. Strategic decision-making in multi-agent settings is a key challenge for large language models (LLMs), particularly when coordination and negotiation must unfold over extended conversations"
---
## Overview
This skill implements concepts from the research paper [[2601.16276](https://arxiv.org/abs/2601.16276)].
## When to Use
- When you need to implement techniques described in this paper
- When working on problems that this research addresses
- When you want to understand the core concepts and methodology
## When NOT to Use
- This skill provides research-level insights; production implementations may require additional engineering
- Some concepts may require significant tuning for specific use cases
- Always evaluate applicability to your specific problem domain
## Key Concepts
The paper addresses: Strategic decision-making in multi-agent settings is a key challenge for large language models (LLMs), particularly when coordination and negotiation must unfold over extended conversations. While recent work has explored the use of LLMs in isolated ...
For detailed methodology, refer to the [full paper](https://arxiv.org/html/2601.16276).
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