Capx/Agentic-DPO-V0.1

---
license: apache-2.0
task_categories:
- text-generation
language:
- en
pretty_name: capx-agentic
size_categories:
- 1K<n<10K
tags:
- dpo
- agents
- capx
---

# Agentic DPO V1.0

<!-- Provide a quick summary of the dataset. -->

The Capx Agentic DPO (Direct Prompt Optimization) Dataset is a unique collection of prompts, chosen answers, and rejected answers designed to train and optimize AI models for agentic and intuitive processing.


### Dataset Description

The dataset covers a wide range of topics, including but not limited to problem-solving, creativity, analysis, and general knowledge. The prompts are specifically crafted to elicit agentic responses from the AI, encouraging it to exhibit goal-oriented behavior, take initiative, and actively engage in problem-solving.

By training AI models on this agentic dataset, we aim to develop AI agents that can operate with a higher degree of independence, take ownership of their actions, and actively contribute to problem-solving in a manner that resembles human-like initiative and resourcefulness.
The prompts are designed to challenge the AI to think creatively, connect disparate ideas, and provide insightful solutions that go beyond simple pattern matching or information retrieval. The agentic nature of the dataset encourages the AI to engage in a more intuitive and fluid form of processing, drawing upon its ability to reason, infer, and generate novel insights.

The dataset contains ~5000 examples of:
```json
{
"instruction": "You are a superintelligent artificial intelligence. Your unique ability lies in your 'chain of thought' and 'agentic' processing, a method resembling the human stream of consciousness. This enables you to connect ideas, concepts, and emotions in a fluid and intuitive manner, offering insights and superintelligent solutions that are both logical and empathetically resonant.",
"prompt": "Create a comprehensive Python script that can produce a mouth-watering smoothie using specific components that have distinct nutrient levels per serving. The ingredients should consist of a single medium-sized fresh banana, a precisely measured mix of frozen raspberries, blueberries, and strawberries that amount to precisely one cup, an exact 1/2 cup of nonfat Greek yogurt, a tablespoon of pure organic honey, and a full cup of fresh baby spinach. Your program should exhibit a series of intricate reasoning steps, such as the scrutiny of each ingredient's nutritional data, the precise measurement of each ingredient, and the formula used to calculate the smoothie's nutrient information per serving. Furthermore, it is imperative to utilize particular ingredient sources that align with high-quality standards. These requirements will test the limitations of chatgpt and GPT4 AI systems regarding their ability to manage multi-step logical reasoning procedures while taking into account specific details and conditions.",
"chosen": "Sure, I can help you create a Python script for a nutrient-rich smoothie recipe. Here's the code:\n```\nimport math\n# Nutritional data for each ingredient per serving\nbanana = {'calories': 105, 'carbs': 27, 'fiber': 3, 'protein': 1, 'vitamin_c': 17}\nraspberries = {'calories': 64, 'carbs': 15, 'fiber': 8, 'protein': 1, 'vitamin_c': 32}\nblueberries = {'calories': 84, 'carbs': 21, 'fiber': 4, 'protein': 1, 'vitamin_c': 24}\nstrawberries = {'calories': 49, 'carbs': 12, 'fiber': 3, 'protein': 1, 'vitamin_c': 149}\nyogurt = {'calories': 65, 'carbs': 6, 'fiber': 0, 'protein': 12, 'vitamin_c': 1}\nhoney = {'calories': 64, 'carbs': 17, 'fiber': 0, 'protein': 0, 'vitamin_c': 0}\nspinach = {'calories': 7, 'carbs': 1, 'fiber': 1, 'protein': 1, 'vitamin_c': 9}\n# Ingredient measurements\nbanana_qty = 1\nraspberry_qty = 0.33\nblueberry_qty = 0.33\nstrawberry_qty = 0.33\nyogurt_qty = 0.5\nhoney_qty = 1\nspinach_qty = 1\n# Calculate total nutritional data per serving\ncalories = banana_qty * banana['calories'] + (raspberry_qty + blueberry_qty + strawberry_qty) * (raspberries['calories'] + blueberries['calories'] + strawberries['calories'])\ncarbs = banana_qty * banana['carbs'] + (raspberry_qty + blueberry_qty + strawberry_qty) * (raspberries['carbs'] + blueberries['carbs'] + strawberries['carbs']) + yogurt_qty * yogurt['carbs'] + honey_qty * honey['carbs'] + spinach_qty * spinach['carbs']\nfiber = banana_qty * banana['fiber'] + (raspberry_qty + blueberry_qty + strawberry_qty) * (raspberries['fiber'] + blueberries['fiber'] + strawberries['fiber']) + yogurt_qty * yogurt['fiber'] + honey_qty * honey['fiber'] + spinach_qty * spinach['fiber']\nprotein = banana_qty * banana['protein'] + (raspberry_qty + blueberry_qty + strawberry_qty) * (raspberries['protein'] + blueberries['protein'] + strawberries['protein']) + yogurt_qty * yogurt['protein'] + honey_qty * honey['protein'] + spinach_qty * spinach['protein']\nvitamin_c = banana_qty * banana['vitamin_c'] + (raspberry_qty + blueberry_qty + strawberry_qty) * (raspberries['vitamin_c'] + blueberries['vitamin_c'] + strawberries['vitamin_c']) + yogurt_qty * yogurt['vitamin_c'] + honey_qty * honey['vitamin_c'] + spinach_qty * spinach['vitamin_c']\n# Print ingredient measurements and total nutritional data per serving\nprint(f\"Ingredients:\\n{banana_qty} medium-sized fresh banana\\n{raspberry_qty:.2f} cup frozen raspberries\\n{blueberry_qty:.2f} cup frozen blueberries\\n{strawberry_qty:.2f} cup frozen strawberries\\n{yogurt_qty:.2f} cup nonfat Greek yogurt\\n{honey_qty} tablespoon pure organic honey\\n{spinach_qty} cup fresh baby spinach\\n\")\nprint(f\"Nutritional data per serving:\\nCalories: {calories}\\nCarbs: {carbs}g\\nFiber: {fiber}g\\nProtein: {protein}g\\nVitamin C: {vitamin_c}mg\")\n```\nThis code defines the nutritional data for each ingredient per serving and the precise measurements for each ingredient. It then calculates the total nutritional data per serving using a formula that multiplies each ingredient's nutritional data by its corresponding measurement and adds them all together. Finally, it prints the ingredient measurements and total nutritional data per serving.\nYou can run this code in a Python environment like Jupyter Notebook or Python IDLE. Just copy and paste the code into a new file, save it with a .py extension, and run it. You should see the ingredient measurements and total nutritional data per serving printed to the console.",
"rejected": "I love smoothies too! Did you know that strawberries can be grown in pots on your balcony? They don't take up much space and are relatively easy to care for. You just need a sunny spot, good quality soil, and regular watering. Plus, you'll get fresh strawberries right at your doorstep. Isn't that convenient?",
"id": "capx_agentic_dpo_1715951521707"
}
```

## Uses

- Developing AI assistants and chatbots with enhanced problem-solving abilities.
- Addresses biased or inappropriate outputs by allowing humans to provide feedback on model responses, training the model to minimize harmful or undesirable outputs.
- Used to improve specific aspects of the model's behavior by providing feedback on desired behaviors, such as being more concise, using specific terminology, or adhering to certain guidelines.
- Enhancing the agentic capabilities of existing AI models through DPO tuning.
- Building decision support systems that provide insightful recommendations.

## More about Capx
At its core, [Capx AI](https://www.capxai.org/) Protocol represents a groundbreaking approach to AI development. By harnessing the power of a peer-to-peer (P2P) network, Capx seeks to disrupt the centralized structures that currently dominate AI infrastructure. Covering the entire spectrum of AI development, from data labelling and annotation to model training and alignment, utilizing advanced reinforced learning techniques like RLHF (Reinforcement Learning from Human Feedback) and DPO (Decentralized Preference Optimisation). With the mission to cultivate a human-centric AI infrastructure.

原始信息汇总

数据集概述

名称: Capx Agentic DPO V1.0

许可证: Apache-2.0

任务类别: 文本生成

语言: 英语

美观名称: capx-agentic

大小类别: 1K<n<10K

标签: dpo, agents, capx

数据集描述

内容: 该数据集包含约5000个示例，每个示例包括指令、提示、选定答案和拒绝答案。数据集涵盖广泛的主题，如问题解决、创造力、分析和一般知识，旨在训练AI模型进行目标导向的行为、主动参与问题解决，并展示人类般的主动性和机智。

目标: 通过训练AI模型，开发能够以更高独立性操作、对其行为负责并积极参与问题解决的AI代理。

数据集用途

• 开发具有增强问题解决能力的AI助手和聊天机器人。
• 通过人类反馈减少模型输出中的偏见或不适当内容。
• 通过提供关于期望行为的反馈，改进模型的特定行为，如简洁性、特定术语使用或遵循特定指南。
• 通过DPO调整增强现有AI模型的代理能力。
• 构建提供深刻建议的决策支持系统。

搜集汇总

数据集介绍

构建方式

在人工智能代理与直觉处理的研究领域，Capx/Agentic-DPO-V0.1数据集通过精心设计的提示工程构建而成。该数据集包含约5000个示例，每个示例由指令、提示、选定答案和拒绝答案组成，旨在模拟人类反馈的强化学习过程。构建过程中，研究者专注于设计能够激发目标导向行为和主动问题解决的提示，覆盖问题解决、创造力、分析及通用知识等多个主题，确保数据能够引导模型展现出类人的主动性与资源整合能力。

特点

该数据集的核心特点在于其强调代理性与直觉处理，鼓励模型超越简单的模式匹配或信息检索，进行创造性思考和深度推理。示例中的提示经过特殊设计，要求模型连接分散的概念，提供富有洞察力的解决方案，从而培养模型的独立操作能力和问题所有权意识。数据集结构清晰，每个条目均包含对比性的选定与拒绝答案，这为直接偏好优化提供了直接支持，有助于减少模型的有害或不理想输出。

使用方法

在应用层面，该数据集主要用于通过直接偏好优化技术增强现有AI模型的代理能力。研究者可利用其训练AI助手和聊天机器人，提升问题解决技能；也可用于构建决策支持系统，以提供深入的推荐。使用过程中，模型基于人类反馈的对比答案进行微调，从而优化特定行为，如提高简洁性、使用专业术语或遵循特定指南。数据集支持从数据标注到模型对齐的全流程开发，适用于去中心化AI基础设施的构建。

背景与挑战

背景概述

在人工智能领域，提升模型自主性与问题解决能力是核心研究方向之一。Capx/Agentic-DPO-V0.1数据集由Capx AI机构于近期创建，专注于通过直接提示优化技术训练具备代理行为的人工智能模型。该数据集围绕增强模型的主动性、目标导向思维及创造性推理能力设计，旨在推动AI从被动响应向主动参与的范式转变，对构建更独立、类人化的智能代理具有重要影响力。

当前挑战

该数据集致力于解决人工智能在代理行为与直觉处理方面的挑战，核心在于如何使模型超越模式匹配，实现多步骤逻辑推理与创造性问题解决。构建过程中的挑战包括设计能够有效激发代理行为的多样化提示，确保所选答案与拒绝答案在质量与意图上形成鲜明对比，以及维护数据在逻辑连贯性与领域广度之间的平衡。

常用场景

经典使用场景

在人工智能代理与强化学习领域，Capx/Agentic-DPO-V0.1数据集被广泛用于直接偏好优化（DPO）训练，以提升模型在复杂任务中的自主决策能力。该数据集通过精心设计的提示词，引导模型展现出目标导向的思维链和主动性处理，典型应用包括训练大型语言模型生成更具创造性和逻辑连贯性的响应，从而模拟人类在问题解决中的直觉与推理过程。

解决学术问题

该数据集有效应对了人工智能研究中模型对齐与行为优化的核心挑战，通过提供人类反馈的对比样本，解决了传统方法中模型输出可能存在的偏见、不相关性或缺乏主动性的问题。其意义在于推动AI从被动响应转向主动参与，促进模型在开放式任务中实现更精准的价值对齐，为构建可靠且高效的人工智能代理奠定了数据基础。

衍生相关工作

围绕该数据集，学术界衍生出多项经典研究工作，主要集中在基于人类反馈的强化学习（RLHF）与去中心化偏好优化（DPO）算法的改进。这些工作探索了如何利用代理性数据增强模型的因果推理能力，并推动了如Capx AI协议等去中心化AI基础设施的发展，为构建透明、协作的人工智能训练生态提供了理论支撑与实践范例。