Self-Distillation Enables Continual Learning [pdf]

airstrike

Both title and abstract feel a little too confident, which ironically makes me more skeptical rather than less. I find the choice of the words "enable" in the title and "establishing" at the end of the abstract to be particularly jarring.

ArchieScrivener

From Jan 2026. This is very interesting: "Empirical Validation. While we cannot verify these theoretically, we evaluate each empirically. We use the Qwen-2.5-7B-Instruct model (Hui et al., 2024) as the base policy and the ToolAlpaca dataset (Tang et al., 2023). In this benchmark, the model receives a tool-API specification and a user request, and must identify the correct tool call. Without demonstrations, the base model solves only 42% of examples. When provided with the appropriate demonstration c for each prompt x , the teacher achieves a 100% success rate. To further test reward proximity, we manually inspected 50 teacher reasoning traces. In all cases, not only were the final tool calls correct, but the intermediate chain-of-thought was valid and semantically grounded. This suggests that the teacher is reconstructing a correct reasoning process rather than merely copying the expert output. These observations provide evidence for the first requirement, that the demonstration-conditioned model behaves as an optimal policy."

greesil

Wtf is a policy? Is this some sort of RL thing that I'm too ML to understand? Gemini tells me it's the probability of the next token for an LLM. Okay then.

teleforce

Fun facts, this paper is cited by Simple Self-Distillation (SSD) paper by Apple [1],[2]. I think it is a bad naming scheme due to the very common SSD namesake and the fact that it belongs to on-policy self-distillation [3]. But again according to the authors their proposed solution is simple because "SSD uses only temperature-shifted samples from the base model and standard cross-entropy training,without privileged context, feedback-conditioned teachers,or auxiliary supervision." The Apple paper also cited another very similar idea of self-distillation paper by UCLA team. Both cited papers namely by MIT & ETH team, and the other by UCLA team proposed novel on-policy self-distillation technique. Interestingly both teams submitted their papers within one day from each other back in January this year to arXiv [4],[5]. No price for guessing who actually published the idea first. IMHO, self-distillation fine-tuning is the future of LLM fine-tuning because it mitigates the forgetfulness of the SFT approach that can be cumbersome for lightweight fine-tuning rather than full post-training of LLM. With the advent and proliferation of plethora open source and open weight LLM foundation models, anyone can fine-tuning these models for domain specialization or sub-specialization (like medicine sub-specialization, law disciplines, branches of architecture practices, etc) [6]. This fine-tuning process can be performed with the minimum resources of 8 H200 or even 4 H100 GPUs as reported respectively in either of the papers [4],[5]. Let's see if we can replicate that with much cheaper arrangements consisting of a couple of DGX Spark, or the latest eight of DGX Spark based nodes arrangement with a total of 1 TB RAM (128 GB x 8) [7],[8]. IMHO, if the results are valid, the self-distillation can be the second best thing happened to LLM after the transformer. [1] Embarrassingly simple self-distillation improves code generation (2026 - 201 comments): https://news.ycombinator.com/item?id=47637757 [2] Embarrassingly Simple Self-Distillation Improves Code Generation: https://arxiv.org/abs/2604.01193 [3] Comment on "Embarrassingly simple self-distillation improves code generation": https://news.ycombinator.com/item?id=47644784 [4] Self-Distillation Enables Continual Learning: https://arxiv.org/abs/2601.19897 [5] Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models: https://arxiv.org/abs/2601.18734 [6] Why domain specific LLMs won't exist: an intuition (2026 - 4 comments): https://news.ycombinator.com/item?id=47649167 [7] NVIDIA DGX Spark Review The GB10 Machine is so Freaking Cool: https://www.servethehome.com/nvidia-dgx-spark-review-the-gb1... [8] BIG AI Cluster Little Power the 8x NVIDIA GB10 Cluster: https://www.servethehome.com/big-cluster-little-power-the-8x...