learnables/learn2learn
A PyTorch Library for Meta-learning Research observed · 2026-08-28
Health v2 · maintenance only
48/100
- Activity 57
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2582
- days_rel: n/a
- days_push: 260
- n_releases_24m: 0
Adoption not part of the score
2893 stars · 359 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
learn2learn is a PyTorch library for meta-learning research, providing utilities for few-shot task creation, high-level wrappers for algorithms like MAML and ProtoNets, and differentiable optimization tools. It includes standardized vision benchmarks and meta-reinforcement learning environments to support fast prototyping and reproducible evaluation.
Use cases
- implement MAML for few-shot image classification
- create few-shot learning tasks from any PyTorch dataset
- run meta-reinforcement learning experiments in gym environments
- benchmark few-shot learning algorithms on Omniglot and MiniImagenet
- implement learned optimizers and meta-descent algorithms
- do parameter-efficient finetuning with LoRA and adapters
- reproduce meta-learning research results fairly
When to choose
- you need well-tested implementations of MAML, ProtoNets, Reptile, ANIL, and related algorithms
- you want to build custom meta-learning algorithms on top of low-level differentiable cloning utilities
- you need standardized few-shot benchmarks with pretrained backbones
- you are doing meta-reinforcement learning research with PyTorch
When to avoid
- you need a production training framework rather than a research library
- you work outside PyTorch (e.g. TensorFlow or JAX)
- you need stable, supported meta-RL environments - the gym module is being reworked
- you just want off-the-shelf pretrained models without meta-learning
Facets
library · maturity active
machine-learning deep-learning llm-training data-science machine-learning deep-learning computer-vision reinforcement-learning python cross-platform pytorch meta-learning maml few-shot-learning meta-reinforcement-learning meta-optimization finetuning lora benchmarks research gpu
10 sources
- readme: https://github.com/learnables/learn2learn · fetched 2026-08-28 · f67ea568ab23
- homepage: http://learn2learn.net · fetched 2026-08-29 · 8373ea76a782
- site_page: https://learn2learn.net/docs/learn2learn · fetched 2026-08-29 · bc574ccb2003
- site_page: https://learn2learn.net/docs/learn2learn.data · fetched 2026-08-29 · 0924d8429278
- site_page: https://learn2learn.net/docs/learn2learn.algorithms · fetched 2026-08-29 · 5b694f8cdc44
- site_page: https://learn2learn.net/docs/learn2learn.optim · fetched 2026-08-29 · 3791344d9d16
- site_page: https://learn2learn.net/docs/learn2learn.nn · fetched 2026-08-29 · d7e71ea75bdf
- site_page: https://learn2learn.net/docs/learn2learn.vision · fetched 2026-08-29 · 95004420acdd
- site_page: https://learn2learn.net/docs/learn2learn.gym · fetched 2026-08-29 · a9d2c8b2283d
- registry_pypi: https://pypi.org/pypi/learn2learn/json · fetched 2026-08-29 · 7f17799b35f9
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| learnables/learn2learn | main | 48 |
For agents
markdown · JSON · MCP: product_card(name="learnables/learn2learn")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem