Self-Distillation Enables Continual Learning [PDF]
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The paper introduces Self-Distillation Fine-Tuning (SDFT), a method enabling models to learn continuously from expert demonstrations without forgetting prior skills. SDFT uses in-context learning by treating a model as its own teacher, generating on-policy training signals from demonstrations. The approach outperforms supervised fine-tuning in both skill acquisition and retention across sequential learning tasks.
- ▪Self-Distillation Fine-Tuning (SDFT) enables on-policy learning directly from expert demonstrations.
- ▪SDFT reduces catastrophic forgetting while improving accuracy on new tasks compared to supervised fine-tuning.
- ▪The method leverages in-context learning, using the model as its own teacher to generate training signals.
- ▪Experiments show SDFT allows a single model to accumulate multiple skills over time without performance decline.
- ▪SDFT establishes on-policy distillation as a practical approach for continual learning from demonstrations.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2601.19897 |
| Publication time | Sun, 17 May 2026 01:19:14 +0000 |
| Retrieval time | 2026-05-17T01:40:19.096Z |
| Last seen | 2026-05-17T01:40:19.096Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Machine Learning arXiv:2601.19897 (cs) [Submitted on 27 Jan 2026] Title:Self-Distillation Enables Continual Learning Authors:Idan Shenfeld, Mehul Damani, Jonas Hübotter, Pulkit Agrawal View a PDF of the paper titled Self-Distillation Enables Continual Learning, by Idan Shenfeld and 2 other authors View PDF HTML (experimental) Abstract:Continual learning, enabling models to acquire new skills and knowledge without degrading existing capabilities, remains a fundamental challenge for foundation models. While on-policy reinforcement learning can reduce forgetting, it requires explicit reward functions that are often unavailable. Learning from expert demonstrations, the primary alternative, is dominated by supervised fine-tuning (SFT), which is inherently off-policy.
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