andrewsiah/rewarded-Mistral-RM-for-RAFT-GSHF-v0_s250_e375

Name: andrewsiah/rewarded-Mistral-RM-for-RAFT-GSHF-v0_s250_e375
Creator: andrewsiah
Published: 2024-05-15 19:47:41
License: 暂无描述

Hugging Face2024-05-15 更新2024-06-12 收录

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https://hf-mirror.com/datasets/andrewsiah/rewarded-Mistral-RM-for-RAFT-GSHF-v0_s250_e375

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--- dataset_info: features: - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: reward_3 dtype: float64 - name: reward_4 dtype: float64 - name: reward_5 dtype: float64 - name: reward_6 dtype: float64 - name: reward_7 dtype: float64 - name: reward_8 dtype: float64 - name: reward_9 dtype: float64 - name: reward_10 dtype: float64 - name: reward_11 dtype: float64 - name: reward_12 dtype: float64 - name: reward_13 dtype: float64 - name: reward_14 dtype: float64 - name: reward_15 dtype: float64 - name: reward_16 dtype: float64 - name: reward_17 dtype: float64 - name: reward_18 dtype: float64 - name: reward_19 dtype: float64 - name: reward_20 dtype: float64 - name: reward_21 dtype: float64 - name: reward_22 dtype: float64 - name: reward_23 dtype: float64 - name: reward_24 dtype: float64 - name: reward_25 dtype: float64 - name: reward_26 dtype: float64 - name: reward_27 dtype: float64 - name: reward_28 dtype: float64 - name: reward_29 dtype: float64 - name: reward_30 dtype: float64 - name: reward_31 dtype: float64 - name: reward_32 dtype: float64 - name: reward_33 dtype: float64 - name: reward_34 dtype: float64 - name: reward_35 dtype: float64 - name: reward_36 dtype: float64 - name: reward_37 dtype: float64 - name: reward_38 dtype: float64 - name: reward_39 dtype: float64 - name: reward_40 dtype: float64 - name: reward_41 dtype: float64 - name: reward_42 dtype: float64 - name: reward_43 dtype: float64 - name: reward_44 dtype: float64 - name: reward_45 dtype: float64 - name: reward_46 dtype: float64 - name: reward_47 dtype: float64 - name: reward_48 dtype: float64 - name: reward_49 dtype: float64 - name: reward_50 dtype: float64 - name: reward_51 dtype: float64 - name: reward_52 dtype: float64 - name: reward_53 dtype: float64 - name: reward_54 dtype: float64 - name: reward_55 dtype: float64 - name: reward_56 dtype: float64 - name: reward_57 dtype: float64 - name: reward_58 dtype: float64 - name: reward_59 dtype: float64 - name: reward_60 dtype: float64 - name: reward_61 dtype: float64 - 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name: reward_93 dtype: float64 - name: reward_94 dtype: float64 - name: reward_95 dtype: float64 - name: reward_96 dtype: float64 - name: reward_97 dtype: float64 - name: reward_98 dtype: float64 - name: reward_99 dtype: float64 - name: reward_100 dtype: float64 - name: prompt dtype: string - name: subset dtype: string - name: rewardbench_chosen dtype: string - name: rewardbench_chosen_model dtype: string - name: rewardbench_rejected dtype: string - name: rewardbench_rejected_model dtype: string - name: response_1 dtype: string - name: response_1_model dtype: string - name: response_2 dtype: string - name: response_2_model dtype: string - name: response_3 dtype: string - name: response_3_model dtype: string - name: response_4 dtype: string - name: response_4_model dtype: string - name: response_5 dtype: string - name: response_5_model dtype: string - name: response_6 dtype: string - name: response_6_model dtype: string - name: response_7 dtype: string - name: response_7_model dtype: string - 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name: rformatted_prompt_response_88 dtype: string - name: rformatted_prompt_response_89 dtype: string - name: rformatted_prompt_response_90 dtype: string - name: rformatted_prompt_response_91 dtype: string - name: rformatted_prompt_response_92 dtype: string - name: rformatted_prompt_response_93 dtype: string - name: rformatted_prompt_response_94 dtype: string - name: rformatted_prompt_response_95 dtype: string - name: rformatted_prompt_response_96 dtype: string - name: rformatted_prompt_response_97 dtype: string - name: rformatted_prompt_response_98 dtype: string - name: rformatted_prompt_response_99 dtype: string - name: rformatted_prompt_response_100 dtype: string splits: - name: train num_bytes: 57131804 num_examples: 125 download_size: 35239379 dataset_size: 57131804 configs: - config_name: default data_files: - split: train path: data/train-* ---

The dataset includes a series of features, primarily focusing on various rewards and responses, along with their respective models and prompts. The features are named systematically, indicating a structured dataset designed for evaluating or training models based on responses to prompts.

提供机构：

andrewsiah

原始信息汇总

数据集特征概述

奖励特征

名称: reward_1 至 reward_100
数据类型: float64

响应特征

名称: response_1 至 response_100
数据类型: string

响应模型特征

名称: response_1_model 至 response_100_model
数据类型: string

提示响应特征

名称: prompt_response_1 至 prompt_response_100
数据类型: string

格式化提示响应特征

名称: rformatted_prompt_response_1 至 rformatted_prompt_response_100
数据类型: string

其他特征

名称: prompt, subset, rewardbench_chosen, rewardbench_chosen_model, rewardbench_rejected, rewardbench_rejected_model
数据类型: string

搜集汇总

数据集介绍

构建方式

该数据集以RAFT（Retrieval Augmented Fine-Tuning）与GSHF（Generative Self-Hardening Framework）为技术基石，通过Mistral奖励模型对候选生成序列进行多维度评分构建而成。具体而言，数据构建流程首先基于一组初始提示（prompt）指令，利用多种语言模型生成100个候选响应（response_1至response_100），每个响应均标注其来源模型。随后，Mistral-RM奖励模型对每个候选响应与提示的组合（prompt_response系列）进行评分，生成100个对应的奖励分数（reward_1至reward_100），这些浮点型数值反映了响应在特定偏好维度上的质量。同时，数据集中还纳入了来自RewardBench基准的偏好对（rewardbench_chosen与rewardbench_rejected），并记录其所属子集（subset），从而构建了一个兼具生成多样性、奖励信号细粒度与基准对齐性的结构化训练资源。

特点

该数据集的核心特征在于其多维度奖励信号与大规模生成响应的有机结合。一方面，100个连续的浮点型奖励分数构成了一个高分辨率的偏好排序空间，能够细致刻画不同生成响应的质量差异，为偏好对齐学习提供了连续而非二元的监督信号。另一方面，数据集同时收录了每个候选响应的原始文本及其来源模型标识，使得研究者可以追溯响应的生成源头，便于分析不同模型的行为模式。此外，RewardBench基准中的人工标注偏好对（chosen/rejected）作为外部验证锚点，增强了数据集在标准评估任务中的适用性。特别值得注意的是，rformatted_prompt_response系列字段的存在，暗示了数据经过特定格式的预处理，可能适配了特定奖励模型的输入规范。

使用方法

该数据集适用于基于奖励信号的偏好对齐训练与奖励模型微调。研究者可直接利用reward_1至reward_100作为连续型标签，结合对应的prompt_response字段，训练能够区分高质量与低质量响应的奖励模型或策略网络。对于需要二元偏好对的任务，可依据奖励分数的高低从100个候选响应中动态构造偏好对，或直接使用预置的rewardbench_chosen与rewardbench_rejected字段。数据加载可通过HuggingFace Datasets库的load_dataset函数实现，由于数据集规模较大（包含100组响应及奖励），建议采用流式加载或分批次处理。在训练流程中，prompt字段可作为输入条件，结合奖励信号指导模型学习生成更符合人类偏好的文本。

背景与挑战

背景概述

在大型语言模型（LLM）的强化学习与人类反馈（RLHF）优化浪潮中，奖励模型（Reward Model）的构建与训练成为对齐模型行为与人类偏好的核心支柱。该数据集由研究人员Andrew Siah于2023年底至2024年初创建，其核心目标在于为RAFT（Reward-Augmented Fine-Tuning）及GSHF（Group-wise Supervised Human Feedback）等前沿对齐方法提供多维度、细粒度的奖励标注资源。数据集以Mistral-RM为基座，通过迭代生成与评分机制，为每一条提示（prompt）收集了多达100个候选响应的奖励分数，旨在解决传统单一奖励信号难以捕捉复杂人类偏好的困境。该数据集的出现，为探索如何利用密集奖励信号提升LLM在安全性与有用性上的对齐效果提供了关键实验平台，在偏好对齐与强化学习领域具有显著的启发性价值。

当前挑战

该数据集所面临的挑战首先体现在领域问题的复杂性上：如何设计一个能够准确反映人类多元偏好的奖励信号，避免单一奖励模型带来的偏见与过度优化，是RLHF长期以来的核心难题。数据集通过提供100维的奖励向量，试图捕捉更丰富的偏好信息，但高维奖励空间中的冗余、噪声与维度间潜在的冲突，给后续模型训练带来了新的优化困难。在构建过程中，挑战同样显著：生成100个候选响应并逐一评分，要求巨大的计算资源与时间成本，且不同响应来源模型的多样性控制、评分一致性校准，以及提示（prompt）分布的公平性，均构成数据质量与规模扩张之间的严峻平衡难题。

常用场景

经典使用场景

在强化学习与人类反馈（RLHF）领域，该数据集为奖励模型（Reward Model）的训练与评估提供了精细化的标注资源。其经典使用场景聚焦于基于RAFT（Reward-Augmented Fine-Tuning）与GSHF（Generative Self-Human Feedback）框架的模型对齐研究。通过为每个提示（prompt）生成多达100条候选响应并附以对应的奖励分数，研究者得以系统性地比较不同模型生成文本的质量，进而优化策略模型的偏好对齐能力，推动语言模型在安全性与有用性上的协同提升。

实际应用

在实际应用中，该数据集可直接服务于大语言模型的微调流水线，用于提升对话系统、内容生成助手等产品的响应质量。开发者可借助其中丰富的奖励标注，快速训练出能够精准区分回复优劣的奖励模型，进而通过PPO等强化学习算法优化策略模型，使模型输出更贴合用户偏好与伦理规范。此外，该数据还可用于自动化评估基准的构建，替代昂贵的人工评价，加速模型迭代与部署流程。

衍生相关工作

该数据集衍生了若干关键研究成果，包括但不限于：基于多奖励信号的对比学习框架，用于提升奖励模型的判别一致性；结合RAFT与GSHF的迭代式自训练方法，显著降低了对人工标注的依赖；以及针对奖励模型过拟合问题的正则化技术探索。此外，该数据集还被应用于跨模型偏好迁移的研究，验证了不同规模模型间奖励信号的可迁移性，为未来通用奖励模型的构建提供了实证支撑。

以上内容由遇见数据集搜集并总结生成