Checkpoints
Saved states of an AI model during training, allowing restarts and experimentation.
Checkpoints are like save points in a video game. When an AI model is being trained, it goes through many steps. A checkpoint is a snapshot of the model's progress at a specific point in training. This snapshot includes all the learned information, like the model's internal settings and parameters.
If something goes wrong during training, or if researchers want to try a different approach from a certain point, they can load a checkpoint. This saves a lot of time and computing power because they don't have to start the entire training process over from the beginning. For example, if a model is being trained for a week and a checkpoint is saved every day, and on the fifth day there's an error, training can resume from the last saved checkpoint on day four.
Checkpoints are discrete, saved states of an AI model's parameters and architecture at a particular stage of the training process. They are typically stored as files containing the model's weights, biases, and optimizer states. Checkpoints enable fault tolerance, allowing training to resume from the last saved state in case of hardware failures or interruptions. They also facilitate experimentation, such as fine-tuning a pre-trained model or exploring different hyperparameter settings from a common starting point.
Checkpoints are becoming more relevant as AI models grow larger and training takes longer. Companies are exploring ways to manage and utilize these saved states for efficiency and safety evaluations, as indicated by discussions around embedding safety evaluators.
- Anthropic and OpenAI want to embed safety evaluators. Will they really be independent?Anthropic · TechCrunch AI ·
- Anthropic Claude
- Anthropic's research touches on safety evaluations, which can be applied to specific checkpoints of their models to assess performance and alignment.
- Model weights
- Model weights are the specific numerical parameters learned by a
- Snapshots
- While similar, 'snapshot' is a more general term for a saved state; i
- Saved models
- This is a broader term that can include checkpoints but also final
The concept of saving intermediate states to resume computation has roots in scientific computing and numerical methods. In deep learning, checkpoints became a standard practice as training processes grew longer and more resource-intensive.
While you can't directly access training checkpoints for most frontier models, you can observe the effect of saving and loading by fine-tuning a smaller, publicly available model. Many platforms allow you to save your fine-tuned model's state, which is analogous to a checkpoint, and then load it to continue working or deploy it.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- PromptRole-play a student struggling with math for AI tutor simulationSimulate a student's math struggles and uncertainty for AI tutor evaluation. · 5 min
- TemplateReusable workflow templates for healthcare teamsTeams can create and share templates for common workflows like documentation or prior authorizations. · 15 min
- TemplateTemplate for building an AI tool tailored to your workA structured approach to defining and building a custom AI tool for specific business needs. · 15 min
- TemplateEvals CSV prompt list for agent skillsA CSV file template for listing prompts used in evaluating agent skills. · 20 min
- HeuristicDouble coding agent task completion with docs linksProviding coding agents with a documentation link significantly increases their task completion rate. · 2 min
Questions people ask
- Why are checkpoints important for AI safety?
- Checkpoints allow developers to save a model's state before introducing potentially risky changes or during rigorous safety testing. If a change leads to unsafe behavior, they can revert to a known safe checkpoint.
- How large are AI model checkpoints?
- Checkpoints can be very large, often ranging from gigabytes to terabytes, because they contain all the learned parameters and optimizer states of a complex neural network.
First seen on Subvolts Sep 16, 2026. Updated Sep 23, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.