当前微调无监督训练出来的语言类模型使它们具对齐人类偏好能力的主流方法是RLHF(Reinforcement Learning from Human Feedback)。然而,RLHF是一个复杂且不稳定的过程,它首先需要训练一个可以反应人类偏好的奖励模型(Reward Model),然后再基于强化学习算法微调无监督的语言类模型,以达到在使得该模型不偏离原始模型太远的情...
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。
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...
Interesting and innovative approach in the training of language models that reflects human preferences and then fine-tuning
- [Generating a Preference Dataset with Llama 3.1 70B and Ollama](ch07/04_preference-tuning-with-dpo/create-preference-data-ollama.ipynb) - [Direct Preference Optimization (DPO) for LLM Alignment](ch07/04_preference-tuning-with-dpo/dpo-from-scratch.ipynb)   7 changes: 1 addition...
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.label_smoothing=Xfor someXbetween 0 and 0.5 when performing DPO training (0 gives the origi...
This is where Direct Preference Optimization (DPO) comes into play. DPO simplifies control by treating the task as a classification problem. Concretely, it uses two models: thetrained model(or policy model) and a copy of it called thereference model. During training, the goal is to make sure...
(3) when multi-objective optimization is performed with maximum thermal comfort and minimum total energy demand when considering adjustment strategies, the severe cold climate has the greatest energy-saving potential (38.1%) and the hot summer and cold winter climate has the largest potential to ...
At a high level, Direct Nash Optimization has many advantages. Firstly, it optimizes towards a more general preference function directly rather than a point-wise reward model, which is limited in its expressibility since it can’t model transitive pr...
The L1-optimal control problem forms a natural framework for formulating space trajectory optimization problems. Based on thruster configurations and the physics of the mass expulsion, several lp variants of the L1 norm of the thrust force can be articulated. Quadratic cost functions are inappropriate...