将rφ(x,y)用πθ(y|x)表示,代入强化学习的reward model损失函数中(σ为sigmoid函数,rθ就是rφ,懒得重打公式了): 最终的dpo loss 如下,最小化即可: 观察损失函数,可见模型结构如下(自己画的,比较简陋) 论文中还有很多详细的推理以及说明,感兴趣的可以细看下论文。 看完以后,想想虽然dpo的方式好训练,但是...
我们提出了直接偏好优化(Direct Preference Optimization,DPO)算法,该算法隐含了与现有 RLHF 算法相同的优化目标(带有 KL-发散约束的奖励最大化),但实施简单,训练直接。从直观上讲,DPO 更新增加了优先响应与非优先响应的相对对数概率,但它包含了一个动态的、按实例计算的重要性权重,从而防止了我们发现的模型退化现象...
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...
- [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' 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...
Interesting and innovative approach in the training of language models that reflects human preferences and then fine-tuning
🔬 主要结论:研究表明TR-DPO在Anthropic HH和TLDR数据集上,与传统的DPO(Direct Preference Optimization)相比,性能提升高达19%,在一系列人类中心指标(如连贯性Coherence、正确性Correctness、具体程度Level of Detail、有用性Helpfulness和无害性Harmlessness)上均有统计学显著提升。
Besides the vanilla DPO algorithm, we support other variants of DPO algorithms including Identity preference optimization (IPO) and Reward-aware preference optimization (RPO). The algorithm is identified with thedpo.preference_lossconfig variable. We support three sorts of RPO algorithms based on the ...
🎓 Direct Preference Optimization While the concept of RLHF has been used in robotics for a long time, it was popularized for LLMs in OpenAI’s paperFine-Tuning Language Models from Human Preferences. In this paper, the authors present a framework where a reward model is trained to approxim...
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...