# facebookresearch/schedule_free

Schedule-Free Optimization in PyTorch

Repository: https://github.com/facebookresearch/schedule_free
Canonical: https://ross.abutalabs.com/products/schedule_free
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-07-28T17:10:29+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 94, release rhythm 35, longevity 63
- inputs: {"age_days": 889, "days_push": 36, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2323, forks 78 (observed 2026-08-28T04:06:37.260801+00:00)

## What it is
A PyTorch library implementing schedule-free optimizers (SGD, AdamW, RAdam variants) that remove the need for learning rate schedules or specifying training length in advance. Based on the 'The Road Less Scheduled' paper, it replaces momentum with interpolation and averaging.

## Use cases
- train deep learning models without tuning learning rate decay schedules
- avoid specifying total training steps in advance
- replace cosine decay schedules in PyTorch training loops
- train LLMs with schedule-free AdamW
- get better test loss than scheduled optimizers at any stopping point

## When to choose
- you use PyTorch and want to eliminate learning rate schedule tuning
- you don't know the total number of training steps ahead of time
- you want to match or beat cosine/linear decay schedules without scheduling

## When to avoid
- you need a Jax implementation (use Optax's version instead)
- you rely on optimizers incompatible with the train/eval mode switching pattern
- you need minimal memory overhead beyond the base optimizer with the reference variants

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, llm-training
- domain: machine-learning, deep-learning, large-language-models
- platform: python
- tags: pytorch-optimizers, schedule-free-learning, learning-rate-schedule, adamw, sgd, research

## Member repositories
- facebookresearch/schedule_free (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:37.260801+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:38:35.924816+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/schedule_free (fetched 2026-08-28T04:06:37.260801+00:00, sha 83271c878042)
- Data as of 2026-08-30T08:39:29.467469+00:00.