rDirect(x,y)=pθ(Yes∣x,y,I),I=‘Is the answer correct (Yes/No)?’ 其中x代表原始问题,y代表原始回答,根据模型输出"yes" token的几率作为Gen RM对回答y的打分。注意这里有个好处是,RM的一大用处是对response质量做排序再做下一步的reject sampling/RLHF,而直接让模型输出"yes
将reward modeling 视为 next-token prediction 是一种将奖励建模转化为序列预测问题的策略。在这种方法中,生成式验证器被训练来预测给定上下文(如当前生成的文本序列)的下一个最佳令牌(token)。每个令牌的预测都基于当前的奖励函数,该函数评估了到目前为止生成的序列的质量。通过这种方式,生成式验证器能够逐步构建出高...
不会取名字取了也重复创建的收藏夹chat o1内容:OpenAI-O1 相关论文03-Generative Verifiers: Reward Modeling as Next-Token Prediction,如果您对当前收藏夹内容感兴趣点击“收藏”可转入个人收藏夹方便浏览
而简单使用现成LLM作为评判者（LLM-as-a-Judge）的方法在推理任务中表现也不佳。
[LG]《Generative Verifiers: Reward Modeling as Next-Token Prediction》L Zhang, A Hosseini, H Bansal, M Kazemi... [Google DeepMind] (2024) http://t.cn/A6RJCDsV #机器学习##人工智能##论文#
Recipes to train reward model for RLHF. Contribute to RLHFlow/RLHF-Reward-Modeling development by creating an account on GitHub.
Finally, compute the steady-state probabilities for the SMP as follows [9]: (7.3)πi=πidhi∑jπjdhj CTMCs and SMPs are rarely used directly to specify a system's model in a typical modeling process because of the associated difficulties. First, the state space can grow much faster ...
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masked language modeling8, where the model predicts missing words in a sentence, and next sentence prediction, which helps the model understand cross-sentence relationships to develop deep contextual understanding. While GPT uses a causal self-attention mechanism, where each token attends only to previ...
Yet it has recently been questioned whether dopamine is truly needed to learn about pleasures or truly ever causes new learning directly, at least as a teaching signal, prediction error, or stamping-in mechanism for stimulus–stimulus or stimulus–response associations (Berridge2007; Hnasko et al....