While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for multilingual medical reasoning applications, hindering their deployment in multilingual healthcare settings. We address this by first introducing CUREMED-BENCH, a high-quality multilingual medical reasoning dataset with open-ended reasoning queries with a single verifiable answer, spanning thirteen languages, including underrepresented languages such ...
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---
name: cure-med-curriculum-informed-reinforcement
title: "CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Dialogue"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.13262"
keywords: [Learning]
description: "While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for multilingual medical reasoning applications, hindering their deployment in multilingual healthcare settings. We address this by first introducing CUREMED-BENCH, a high-quality multilingual medical reasoning dataset with open-ended reasoning queries with a single verifiable answer, spanning thirteen languages, including underrepresented languages such as ..."
---
## Overview
This skill covers cure-med: curriculum-informed reinforcement learning for multilingual dialogue. It addresses critical challenges in autonomous agent development.
## Key Concepts
The paper introduces novel approaches to:
- Agent evaluation and benchmarking
- Improving agent efficiency and reasoning
- Designing robust agent systems
## When to Use
Use this when working on:
- Agent-based systems and evaluation
- Autonomous reasoning and planning
- Multi-agent frameworks
## When NOT to Use
- Non-agent applications
- Tasks requiring implementation code (see the paper)
## References
- Paper: https://arxiv.org/abs/2601.13262
- PDF: https://arxiv.org/pdf/2601.13262
