最近的一个论文 Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints 指出说: 实现与 human 对齐的常见技术是 RLHF,最近的论文提出了 DPO 方法,这种方法是 RLHF + Reverse KL 的近似,DPO 的优势是不再需要分两阶段训练 reward 模型进而相比 RLHF 大为简化。本文章...
理论分析指出，Forward KL倾向于mean-seeking，即学生模型会尽力拟合多个输出模式，而Reverse KL则倾向于mode-seeking，更专注于拟合特定的输出模式。这在不同任务中表现出了明显的区别。然而，MiniLLM等文章提出了使用Reverse KL的理由：对于LLM而言，输出空间更为复杂多变，存在更多模式。在使用Forward KL时...
导言:近来有很多文章尝试做LLM的蒸馏,有几篇文章都提到说,使用Reverse KL会比Forward KL好,并且给出了自己的理由,事实真的如此么? FKL vs RKL 先介绍介绍基础知识,KL散度在知识蒸馏KD中有广泛应用,也广为大家所使用。不过,KL散度并不是对称的,正向KL不等于反向KL。这里介绍一个讲的比较好的blog: https://di...
TLDR: 实现与human对齐的常见技术是RLHF,最近的方法提出了DPO方法,这种方法是RLHF+Reverse KL的近似,DPO的优势是不再需要分两阶段训练reward模型进而相比RLHF大为简化。本文章发现,考虑更general的KL散度(f散度)时,RLHF也可以简化为DPO的形式。 建议阅读一下 RLHF的基本原理,为什么RLHF比较复杂,DPO时如何作为RLHF...
First, we show that the appropriate training criterion for Prior Networks is the reverse KL-divergence between Dirichlet distributions. This addresses issues in the nature of the training data target distributions, enabling prior networks to be successfully trained on classification tasks with arbitrarily...
GO 系列 Scorpius GO (5) Canis GO (4) CG410 CG420 CG425 CG410 R Sato GO (2) KL 安全灯系 特种照明 前灯 尾灯 嵌入式工作灯 卤素工作灯 Filter Clear GO 系列 / Canis GO / CANIS GO 410 REVERSE CANIS GO 410 REVERSEOur entry-level Canis GO 410 Reverse is as good as it gets. While ...
本期code:https://github.com/chunhuizhang/deeplearning_math/blob/main/tutorials/prob_stats/forward_reverse_kl_div.ipynbhttps://github.com/chunhuizhang/deeplearning_math/blob/main/tutorials/prob_stats/kl, 视频播放量 2195、弹幕量 0、点赞数 78、投硬币枚数
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Paper tables with annotated results for Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
with this balance, leading to unintended information loss or partial forgetting. To address this challenge, we propose RKLD, a novel \textbf{R}everse \textbf{KL}-Divergence-based Knowledge \textbf{D}istillation unlearning algorithm for LLMs targeting the unlearning of personal information. Through...