SFT or reinforcement fine-tuning for continual post-training?
An article published on January 11, 2026 compares SFT and reinforcement fine-tuning in continual post-training of foundation models. Its title claims reinforcement fine-tuning naturally mitigates forgetting.
Source: Comparação entre SFT e reinforcement fine-tuning no continual post-training (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on January 11, 2026, the article compares supervised fine-tuning (SFT) and reinforcement fine-tuning in continual post-training of foundation models. Its title claims reinforcement fine-tuning naturally mitigates forgetting; the comparison is presented as support for choosing methods to adapt models to evolving tasks.
A company could implement an evaluation process to compare the approaches on its tasks, monitor regressions in prior capabilities, and record results across updates. This evidence can inform which method to use and when to retrain, without assuming one approach will be better in every case.
How the consultancy can help
Wendelmaques can diagnose a model adaptation process, define metrics and evaluations to compare methods, and deliver a scoped implementation of testing and monitoring, with operation as agreed.
Next step
Send a short description of your tasks and model update process to receive a scoped proposal.
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