facebookresearch/higher

Health v2 · maintenance only

Flags: no_releases archived

Adoption not part of the score

1626 stars · 125 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

higher is a PyTorch library that enables higher-order gradients over losses spanning entire training loops rather than individual steps. It provides tools to make torch.nn modules stateless and differentiable versions of torch.optim optimizers for meta-learning approaches.

Use cases

• implement meta-learning algorithms like MAML in pytorch
• compute gradients through unrolled optimization loops
• differentiate through adam or sgd update steps
• make torch nn modules functional for gradient tracking
• hyperparameter optimization via gradient-based methods
• second-order gradient computation across training steps

When to choose

• you need to backpropagate through multiple model update steps
• you are implementing meta-learning or gradient-based hyperparameter optimization
• you want differentiable optimizers compatible with existing torch code

When to avoid

• you only need standard first-order training without gradients through updates
• you are not using pytorch
• you need actively maintained tooling, as the last release was in 2022

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning artificial-intelligence python pytorch meta-learning differentiable-optimization higher-order-gradients hyperparameter-optimization

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markdown · JSON · MCP: product_card(name="facebookresearch/higher")