iSDFT proposes self-distillation for continual learning
A continual-learning method for LLMs described as on-policy information-proximal self-distillation. Its repository provides training code, checkpoints, and evaluation results for exploration.
iSDFT is presented as a continual-learning method for LLMs, described as on-policy information-proximal self-distillation. Its repository provides training code, checkpoints, and evaluation results.
To study the proposal, start by identifying the training steps and required parameters in the code. Use the reported results as a reference for understanding what was evaluated.
If you run experiments, record configurations and results and compare them consistently. Checkpoints can help explore the method, but the source does not claim it outperforms other approaches.
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