Self-distillation may support continual learning without rewards
A post published on February 16, 2026 describes a self-distillation technique for training models on new tasks without a reward function, with the aim of reducing catastrophic forgetting.
Source: Self-distillation para continual learning sem funções de recompensa (arxiv.org). Text prepared with AI from this source.
What happened and what to do
A post dated February 16, 2026 describes a self-distillation method for continual learning. A model conditioned on a demonstration acts as the teacher; the same model, without that demonstration, generates text as the student and is trained to approximate the teacher’s token distributions. The approach aims to reduce catastrophic forgetting as new tasks are added, without defining a reward function.
A company adapting models to successive tasks could implement an evaluation pipeline to compare performance on new and previous tasks, track data and model versions, and monitor regressions during training. A controlled test can establish whether distillation meets requirements before it becomes part of the update process.
How the consultancy can help
Wendelmaques can diagnose the update process and forgetting risks, define an evaluation with acceptance criteria, and propose the implementation and operation of a training and monitoring pipeline suited to the client’s environment.
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