We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.

The long-term goal of artificial intelligence is to solve advanced real-world challenges. Games have served as stepping stones along this path for decades, from Backgammon (1992) to Chess (1997) to Atari (2013)[1-3]. In 2016, AlphaGo defeated the world champion at Go using deep reinforcement learning and Monte Carlo tree search[4]. In recent years, reinforcement learning (RL) models have tackled tasks as varied as robotic manipulation[5], text summarization [6], and video games such as Starcraft[7] and Minecraft[8]. Relative to previous AI milestones like Chess or Go, complex video games start to capture the complexity and continuous nature of the real world. Dota 2 is a multiplayer real-time strategy game produced by Valve Corporation in 2013, which averaged between 500,000 and 1,000,000 concurrent players between 2013 and 2019. The game is actively played by full time professionals; the prize pool for the 2019 international championship exceeded $35 million (the largest of any esports game in the world)[9, 10]. The game presents challenges for reinforcement learning due to long time horizons, partial observability, and high dimensionality of observation and action spaces.

Generalizing between tasks remains difficult for state of the art deep reinforcement learning(RL) algorithms. Although trained agents can solve complex tasks, they struggle to transfer their experience to new environments. Agents that have mastered ten levels in a video game often fail catastrophically when first encountering the eleventh. Humans can seamlessly generalize across such similar tasks, but this ability is largely absent in RL agents. In short, agents become overly specialized to the environments encountered during training. That RL agents are prone to overfitting is widely appreciated, yet the most common RL benchmarks still encourage training and evaluating on the same set of environments. We believe there is a need for more metrics that evaluate generalization by explicitly separating training and test environments. In the same spirit as the Sonic Benchmark (Nichol et al., 2018), we seek to better quantify an agent's ability to generalize.