GitHub Official Repo:https://github.com/eric-mitchell/direct-preference-optimization Direct Preference Optimization: Your Language Model is Secretly a Reward Model:https://arxiv.org/abs/2305.18290 Kullback–Leibler divergence Wiki:https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence Fine-...
原始的强化学习目标函数如下: 当prompt=x,待训练的model(πθ)产生response=y。rφ(x,y) 是reward model给该response打的score,πθ(y|x)是待训练model 在输入为x时产生的likelyhood,πref是refer model在输入为x时产生的likelyhood。Dkl为kl散度,衡量两个likelyhood之间的差异,防止待训练的模型投机取巧,迎合rewa...
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 ...
This branch is up to date witheric-mitchell/direct-preference-optimization:main. README License DPO: Direct Preference Optimization New:in addition to the original DPO algorithm, this repo now supports'conservative' DPOandIPO. For conservative DPO, you just need to additionally pass the parameterloss...
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,...
Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO), a simpler alternative to RLHF which directly optimizes a policy that best satisfies human preferences under a classification objective. We re-formulate DPO to account for a diffusion model notion of likelihood,...
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。