Continual learning in LLMs with iSDFT
A repository published on September 22, 2026 presents a self-distillation method for continual learning in LLMs, with training code, checkpoints, and evaluations.
Source: iSDFT: Self-Distillation Information-Proximal para Continual Learning (github.com). Text prepared with AI from this source.
What happened and what to do
The GitHub repository describes iSDFT, a continual-learning method for LLMs based on on-policy self-distillation with information proximity. Published on September 22, 2026, it includes training code, checkpoints, and evaluation results for exploring the method.
A company could adapt this approach to update models while keeping performance on earlier tasks in view. A project could define evaluation sets, automate training and comparison cycles, monitor regressions, and produce controlled checkpoints, with acceptance criteria before deployment.
How the consultancy can help
Wendelmaques can assess the use case and regression risks, define an appropriate evaluation approach, and propose the implementation and operation of training, validation, and monitoring pipelines.
Next step
Send a short description of your continual-learning case; Wendelmaques can assess the scope and prepare an implementation and operation proposal.
Let's assess your project
I reply with scope and a proposal.