大语言模型-RLHF(六)-PPO(Proximal Policy Optimization)原理&实现&代码逐行注释 Laniakea 4 人赞同了该文章 一,前言 从open AI 的论文可以看到,大语言模型的优化,分下面三个步骤,SFT,RM,PPO,我们跟随大神的步伐,来学习一下这三个步骤和代码实现,本章介绍PPO代码实现。 上章我们介绍了PPO算法的公式,其...
While direct policy optimization methods exist, state-ofthe-art LLMs adopt RL-based methods (usually PPO) in RLHF to train the policy to generate good responses guided by a reward model learned from preference data. The main challenge of these methods is the inaccuracy of the intermediate ...
前面我们提到,PPO很像你在真实棋盘上有一位教练随时指导,边对弈边在真实环境中改进策略(在线学习);而DPO则更像你坐在家里研究一本棋谱(离线数据),通过已有的胜负对照来推断如何改进走法。 本节就来具体推导 DPO(Direct Preference Optimization)的数学原理,解释它在和PPO(或更一般的 RLHF 思路)对比时有何长处与...
An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DPO & LoRA & RingAttention & RFT) reinforcement-learningraylibtransformersproximal-policy-optimizationlarge-language-modelsreinforcement-learning-from-human-feedbackvllmopenai-o1 ...
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And taking the max action of a continuous output is an optimization problem itself! Instead, with a policy gradient, we output a **probability distribution over actions.** ### The Disadvantages of Policy-Gradient Methods Naturally, Policy Gradient methods have also some disadvantages: - **Policy...
In the next section, we shall talk about the key differences in the two main kind of policies: / On-policy reinforcement learning Off-policy reinforcement learning On-Policy VS Off-Policy Comparing reinforcement learning models for hyperparameter optimization is an expensive affair, and often practic...
Using these methods, we train a evaluation model with minimal expert-labeled data, which then effectively labels nine times more preference pairs for further RLHF training. For instance, our model using Direct Preference Optimization (DPO) gains around over 1% average improvement on AlpacaEval2, ...
ppo原理前一章节已经讲了,传送门ChatGLM-RLHF(六)-PPO(Proximal Policy Optimization)原理&实现&代码逐行注释_Pillars-Creation的博客-CSDN博客 需要注意的就是,因为训练时候需要加载sft和rm两个模型, 你需要一个大一点显存的gpu,本例在A100,40G显存上跑通。如果显存小了容易报显存不足的错误。
二、从基本的 policy-based 方法到 RLHF 中使用的 PPO 1. REINFORCE方法 2. MC方法 3. Actor-Critic 方法 3.1 融入Q-learning的PolicyGradient 3.2 A2C:(Advantage Actor-Critic) 3.3 A3C: Asynchronous Advantage Actor-Critic 3.4 off-policy 4. TRPO(信任区域策略优化,Trust Region Policy Optimization) 4.1...