DPO(Direct Preference Optimization)算法 DPO(直接偏好优化)是一种创新的语言模型训练方法,与传统的PPO算法相比,它不需要额外训练奖励模型(Reward Model)。因为语言模型本身隐含了奖励函数的信息(具体下面介绍)。DPO直接将隐含的奖励函数损失转化为对策略的损失,无需训练一个单独的奖励模型。
本文主要介绍RLHF的屌丝版本:DPO,DPO的目的和RLHF一样都是可以让模型的输出更偏向于人类喜好,但是相比RLHF,它的理解难度、实现难度和资源占用都非常友好,所以还是很有必要试一试的。 本文重点介绍DPO的使用场景和最简代码,复杂的公式推导暂不涉及。 2 当前关于LLM的共识 现在主流的LLM,比如chatglm、chinese-alpaca,...
但是RLHF面临缺陷:RLHF 是一个复杂且经常不稳定的过程,首先拟合反映人类偏好的奖励模型,然后使用强化学习微调大型无监督 LM,以最大化这种估计奖励,而不会偏离原始模型太远。为解决这一问题,提出一个直接偏好优化 (DPO) 的新算法:通过利用奖励函数与最优策略之间的映射关系,证明这个受限的奖励最大化问题可以通过单...
Preference learning:The model is fine-tuned on preference data ideally sourced from the same distribution as the SFT examples. Unlike RLHF, in which a reward model is trained first for policy optimization, DPO directly adds preference information into the optimization process without the intermediate...
几篇论文实现代码:《Direct Preference-based Policy Optimization without Reward Modeling》(NeurIPS 2023) GitHub: github.com/snu-mllab/DPPO [fig7] 《Solving Math Word Problems via Cooperative Reason...
Direct Preference Optimization Since we are using re-ranking and answers can potentially be in either index, we implemented a pattern called direct preference optimization which can help add additional context for both positive and negative responses provided by the LLM. This data can be added to ...
DPO: Direct Preference Optimization New: in addition to the original DPO algorithm, this repo now supports 'conservative' DPO and IPO. For conservative DPO, you just need to additionally pass the parameter loss.label_smoothing=X for some X between 0 and 0.5 when performing DPO training (0 giv...
Direct Preference Optimization (DPO) improves the alignment of large language models (LLMs) with human values by training directly on human preference datasets, eliminating the need for reward models. However, due to the presence of cross-domain human preferences, direct continual training can lead ...
we introduce Token-level Direct Preference Optimization (TDPO), a novel approach to align LLMs with human preferences by optimizing policy at the token level. Unlike previous methods, which face challenges in divergence efficiency, TDPO incorporates forward KL divergence constraints for each token,...
3 Direct Preference Optimization 可以看到在RLHF中,整个过程分为两步,需要先训练一个RM,然后根据RM利用强化学习算法训练LM。DPO最大的贡献在于提出了一种方法,可以跳过训练RM的过程,直接利用人类偏好数据训练LM,并在理论上通过DPO训练得到的LM,和通过RLHF得到的LM是一致的,二者共享同一个训练目标,只不过在DPO中,...