All is Not Lost: LLM Recovery without Checkpoints
Authors: name: Nikolay Blagoev; name: Oğuzhan Ersoy; name: Lydia Yiyu Chen
Published: 2025-06-18 13:48:33

Abstract: Training LLMs on decentralized and wimpy computation nodes, e.g., multiple
on-spot instances, lowers the training cost and enables model democratization.
The inevitable challenge here is the churn of nodes due to failures and the
operator's scheduling policies, leading to losing a stage - a part of the
model. The conventional approaches to recover from failures are to either use
checkpointing, where periodically a copy of the entire model is sent to an
additional storage, or redundant computation. These approaches yield
significant communication and/or computation overhead even in non-failure cases
and scale poorly in settings with large models. In this paper, we propose,
CheckFree, an efficient recovery method where a failing stage is substituted by
a weighted average of the closest neighboring stages. In contrast to the state
of the art, CheckFree requires no additional computation or storage. However,
because of the nature of averaging neighbouring stages, it can only recover
failures of intermediate stages. We further extend our method to CheckFree+
with out-of-order pipeline execution to tolerate crashes of the first and last
stages. Thanks to out-of-order pipelining, behaviour of those stages is
mimicked by their neighboring ones, which allows CheckFree+ to recover them by
simply copying the weights from the immediate neighbour. To be able to recover
the (de)embedding layers, CheckFree+ copies those layers to the neighboring
stages, which requires relatively small storage overhead. We extensively
evaluate our method on LLaMa models of model sizes from 124M to 1.5B with
varying failure frequencies. In the case of low and medium failure rates
(5-10%), CheckFree and CheckFree+ outperform both checkpointing and redundant
computation in terms of convergence in wall-clock time by over 12%. Both of our
proposals can be run via our code available at:
https://github.com/gensyn-ai/CheckFree.