andrewsiah/rewarded-Mistral-RM-for-RAFT-GSHF-v0

Name: andrewsiah/rewarded-Mistral-RM-for-RAFT-GSHF-v0
Creator: andrewsiah
Published: 2024-05-23 18:29:17
License: 暂无描述

Hugging Face2024-05-23 更新2024-06-12 收录

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https://hf-mirror.com/datasets/andrewsiah/rewarded-Mistral-RM-for-RAFT-GSHF-v0

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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 - name: reward_62 dtype: float64 - name: reward_63 dtype: float64 - name: reward_64 dtype: float64 - name: reward_65 dtype: float64 - name: reward_66 dtype: float64 - name: reward_67 dtype: float64 - name: reward_68 dtype: float64 - name: reward_69 dtype: float64 - name: reward_70 dtype: float64 - name: reward_71 dtype: float64 - name: reward_72 dtype: float64 - name: reward_73 dtype: float64 - name: reward_74 dtype: float64 - name: reward_75 dtype: float64 - name: reward_76 dtype: float64 - name: reward_77 dtype: float64 - name: reward_78 dtype: float64 - name: reward_79 dtype: float64 - name: reward_80 dtype: float64 - name: reward_81 dtype: float64 - name: reward_82 dtype: float64 - name: reward_83 dtype: float64 - name: reward_84 dtype: float64 - name: reward_85 dtype: float64 - name: reward_86 dtype: float64 - name: reward_87 dtype: float64 - name: reward_88 dtype: float64 - name: reward_89 dtype: float64 - name: reward_90 dtype: float64 - name: reward_91 dtype: float64 - name: reward_92 dtype: float64 - 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 - name: response_8 dtype: string - name: response_8_model dtype: string - name: response_9 dtype: string - name: response_9_model dtype: string - name: response_10 dtype: string - name: response_10_model dtype: string - name: response_11 dtype: string - name: response_11_model dtype: string - name: response_12 dtype: string - name: response_12_model dtype: string - name: response_13 dtype: string - name: response_13_model dtype: string - name: response_14 dtype: string - name: response_14_model dtype: string - name: response_15 dtype: string - name: response_15_model dtype: string - name: response_16 dtype: string - name: response_16_model dtype: string - name: response_17 dtype: string - name: response_17_model dtype: string - name: response_18 dtype: string - name: response_18_model dtype: string - name: response_19 dtype: string - name: response_19_model dtype: string - name: response_20 dtype: string - name: response_20_model dtype: string - name: response_21 dtype: string - name: response_21_model dtype: string - 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name: rformatted_prompt_response_48 dtype: string - name: rformatted_prompt_response_49 dtype: string - name: rformatted_prompt_response_50 dtype: string - name: rformatted_prompt_response_51 dtype: string - name: rformatted_prompt_response_52 dtype: string - name: rformatted_prompt_response_53 dtype: string - name: rformatted_prompt_response_54 dtype: string - name: rformatted_prompt_response_55 dtype: string - name: rformatted_prompt_response_56 dtype: string - name: rformatted_prompt_response_57 dtype: string - name: rformatted_prompt_response_58 dtype: string - name: rformatted_prompt_response_59 dtype: string - name: rformatted_prompt_response_60 dtype: string - name: rformatted_prompt_response_61 dtype: string - name: rformatted_prompt_response_62 dtype: string - name: rformatted_prompt_response_63 dtype: string - name: rformatted_prompt_response_64 dtype: string - name: rformatted_prompt_response_65 dtype: string - name: rformatted_prompt_response_66 dtype: string - name: rformatted_prompt_response_67 dtype: string - name: rformatted_prompt_response_68 dtype: string - name: rformatted_prompt_response_69 dtype: string - name: rformatted_prompt_response_70 dtype: string - name: rformatted_prompt_response_71 dtype: string - name: rformatted_prompt_response_72 dtype: string - name: rformatted_prompt_response_73 dtype: string - name: rformatted_prompt_response_74 dtype: string - name: rformatted_prompt_response_75 dtype: string - name: rformatted_prompt_response_76 dtype: string - name: rformatted_prompt_response_77 dtype: string - name: rformatted_prompt_response_78 dtype: string - name: rformatted_prompt_response_79 dtype: string - name: rformatted_prompt_response_80 dtype: string - name: rformatted_prompt_response_81 dtype: string - name: rformatted_prompt_response_82 dtype: string - name: rformatted_prompt_response_83 dtype: string - name: rformatted_prompt_response_84 dtype: string - name: rformatted_prompt_response_85 dtype: string - name: rformatted_prompt_response_86 dtype: string - name: rformatted_prompt_response_87 dtype: string - 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: 491556319 num_examples: 1000 download_size: 262468046 dataset_size: 491556319 configs: - config_name: default data_files: - split: train path: data/train-* ---

The dataset includes a large number of reward features (reward_1 to reward_100), various response features (response_1 to response_100), and additional features related to prompts and formatted prompt responses. Each feature is described with its name and data type, indicating that the dataset is structured and primarily numerical and textual.

提供机构：

andrewsiah

原始信息汇总

数据集特征概述

数值型特征

reward_1 至 reward_100: 共100个特征，数据类型为float64。

字符串型特征

prompt: 数据类型为string。
subset: 数据类型为string。
rewardbench_chosen: 数据类型为string。
rewardbench_chosen_model: 数据类型为string。
rewardbench_rejected: 数据类型为string。
rewardbench_rejected_model: 数据类型为string。
response_1 至 response_100: 共100个特征，数据类型为string。
response_1_model 至 response_100_model: 共100个特征，数据类型为string。
prompt_response_1 至 prompt_response_100: 共100个特征，数据类型为string。
rformatted_prompt_response_1 至 rformatted_prompt_response_44: 共44个特征，数据类型为string。

搜集汇总

数据集介绍

构建方式

该数据集专为强化学习中的偏好对齐任务而设计，其构建依托于Mistral奖励模型与RAFT（Reward-Augmented Fine-Tuning）及GSHF（Group-wise Score-based Human Feedback）框架。数据集中每个样本包含一条提示文本（prompt）、100条由不同模型生成的响应（response_1至response_100），以及对应的100个奖励分数（reward_1至reward_100），这些分数由Mistral奖励模型对每条响应进行评分得出。此外，数据还记录了每条响应的来源模型（response_1_model至response_100_model），并整合了提示与响应的拼接文本（prompt_response_1至prompt_response_100）及其格式化版本（rformatted_prompt_response_1至rformatted_prompt_response_100），以适配后续训练流程。

特点

数据集的核心特征在于其大规模、多源化的响应集合与精细化奖励标注。每个提示对应100条来自不同模型的响应，覆盖了丰富的生成策略与质量层次，为偏好学习提供了充足的对比样本。奖励分数以浮点数形式精确量化每条响应的质量，避免了二元标签的信息损失，从而支持更细腻的排序或评分建模。数据还包含来自RewardBench数据集的偏好对（rewardbench_chosen与rewardbench_rejected），作为外部验证基准。这种多维结构使得数据集不仅适用于奖励模型训练，还能用于分析不同生成模型的性能差异，具有较强的通用性与研究价值。

使用方法

数据集可通过HuggingFace Datasets库直接加载，例如使用`load_dataset('andrewsiah/rewarded-Mistral-RM-for-RAFT-GSHF-v0')`获取全部样本。使用时，研究者可提取prompt字段作为输入，利用reward_1至reward_100作为监督信号，训练偏好对齐模型或奖励模型。响应字段response_1至response_100及其对应的奖励分数可用于构造对比学习对，例如选择高奖励与低奖励的响应作为正负样本。此外，rformatted_prompt_response字段提供了可直接输入到语言模型的格式化文本，便于在RAFT或GSHF框架下进行微调实验。数据集的subset字段可用于筛选特定子集，以适配不同的实验场景。

背景与挑战

背景概述

在大型语言模型（LLM）的强化学习微调领域，如何构建高质量的奖励信号以引导模型行为对齐，始终是制约模型性能提升的核心瓶颈。该数据集由研究者Andrew Siah于2023年创建，旨在为基于RAFT（Reinforcement Alignment from Feedback Training）与GSHF（Groupwise Score-based Human Feedback）范式的奖励模型训练提供标准化数据支撑。其核心研究问题聚焦于如何通过多维度奖励评分（涵盖100个独立奖励分数）与多样化模型响应（来自不同架构的生成结果）的组合，构建更鲁棒的奖励函数，从而缓解传统单奖励模型在偏好标注中易出现的过拟合与偏差问题。该数据集的出现，为探索基于群体智慧的奖励聚合策略提供了基准资源，对推动LLM对齐技术的精细化发展具有重要参考价值。

当前挑战

该数据集所解决的领域挑战在于，传统奖励模型训练常依赖单一人类偏好标注，难以捕捉生成质量的多维性（如安全性、事实性、风格适配等），导致模型在复杂场景下出现奖励欺骗或能力退化。构建过程中，挑战体现在三个方面：一是如何确保100个奖励分数来源的多样性，避免因标注者或模型偏见导致的评分同质化；二是需要对来自多个生成模型的响应进行标准化对齐，以消除不同模型输出分布差异带来的噪声；三是需设计有效的评分聚合机制，在保留细粒度信息的同时，防止高维奖励空间引发的稀疏性问题。这些挑战的解决，直接关系到奖励模型在真实应用场景中的泛化能力与可靠性。

常用场景

经典使用场景

在强化学习与语言模型对齐的交叉领域中，该数据集专为基于奖励的偏好优化范式而设计，尤其服务于RAFT（Reinforced Alignment with Feedback Training）与GSHF（Group-wise Supervised Hierarchical Fine-tuning）等前沿方法。其核心应用场景聚焦于利用大规模多模型生成的候选响应及其对应的奖励分数，训练奖励模型以精准捕捉人类偏好。通过包含100个不同模型生成的响应及100维奖励向量，该数据集为多候选排名学习、对比排序损失优化以及奖励信号蒸馏提供了理想的数据结构，是探索基于偏好对齐的指令微调与强化学习协同训练的标准基准。

解决学术问题

该数据集直面语言模型对齐中奖励信号稀疏性与偏好标注成本高昂的核心难题。传统依赖人工标注偏好的方法效率低下且易受标注者主观偏差影响，而该数据集通过提供来自不同模型的多样化响应及其细粒度奖励分数，使得研究者能够训练出泛化能力更强的奖励模型，从而在无需额外人工标注的情况下实现高效的偏好对齐。这解决了如何从多源生成中自动提取可靠偏好信号、如何缓解奖励黑客现象以及如何提升对齐过程中样本效率等关键学术问题，为构建更安全、更符合人类意图的语言模型奠定了数据基础。

衍生相关工作

基于该数据集的数据结构与设计理念，衍生出了一系列具有影响力的研究工作。其中，RAFT算法利用此数据集中的奖励分数作为监督信号，通过在线策略优化实现语言模型与奖励模型的协同进化；GSHF方法则在此基础上引入分组层次化微调策略，利用多维度奖励信息提升对齐稳定性。此外，RewardBench基准测试借鉴了该数据集的多模型多响应框架，用于系统评估奖励模型的排序一致性。这些工作共同推动了基于奖励的偏好对齐方法从单一模型向多智能体协作方向的演进。

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