In this work, we investigate the merits of explicitly optimizing for inference time algorithmic performance during model training. We show how optimizing for inference time performance can improve overall model efficacy. We consider generic inference time objectives with $k$ samples, with a focus on pass@$k$ and majority voting as two main applications. With language model training on reasoning datasets, we showcase the performance trade-off enabled by training with such objectives. When training on code generation tasks, we show that the approach significantly improves pass@$k$ objectives compared to the baseline method.

ChatGPT by OpenAl has been exploded all over the web recently and is even said to take the place of Google. ChatGPT is a natural language processing (NLP) model developed by OpenAI. It is designed to generate human-like responses to text input, allowing users to engage in natural, conversational interactions with the model. GPT(Generative Pretrained Transformer) is a large, deep learning-based model that was trained using unsupervised learning on a massive dataset of text. It uses a transformer architecture, which is a type of neural network that is well-suited to natural language processing tasks.

We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response. We are excited to introduce ChatGPT to get users' feedback and learn about its strengths and weaknesses. During the research preview, usage of ChatGPT is free.

OpenAI has released a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.