Self-Distillation Enables Continual Learning [pdf]

https://arxiv.org/abs/2601.19897

by teleforce • 2 months ago

109 26 2 months ago

airstrike2• 2 months ago

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• 2 months ago

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."

greesil5• 2 months ago

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.

mountainriver• 2 months ago

What is this comment? It’s an RL paper, these are standard RL terms

greesil5• 2 months ago

It's a comment. On Hacker News. Not the RL subreddit, or whatever. I'm just amazed at the jargon. I'm sure it's useful, but one could just call it model output.

Ifkaluva• 2 months ago

It’s quite common these days to treat an LLM as a policy in the sense that it takes as a “state” the previous context, and its task is to choose a continuation, as an “action”. It gets a “reward” from a reward model that was trained on human preferences, or from a verifiable source, such as passing test cases.

This framing has been active for several years, as it’s the framing that enables RLHF and RLVR. RLHF itself is quite old, I think since the original chatGPT.

antonvs5• 2 months ago

Gemini didn't really say that exactly, did it? Because it's oversimplified to the point of being wrong.

“Policy” here refers to a probability distribution, i.e. a function that, given some context, assigns probabilities to possible next tokens. It's what a model’s behavior looks like when viewed through an RL lens.

The paper discusses “on-policy” and “off-policy” training, which is central to its idea.

Off-policy training is what happens in standard supervised fine-tuning (SFT): the model is trained on examples that were produced independently of the model. This means that the examples have a different distribution than what the model produces. This can have a negative effect on previously learned capabilities.

On-policy training (in this context) uses data generated by the model itself. It samples the model’s own outputs, scores them against whatever results are being trained for, and updates the model based on those scores. This reinforces certain aspects of the model's own pretrained behavior, so is a "gentler" way to change the model's behavior. The authors claim that this reduces "catastrophic forgetting" and other negative consequences of SFT.

antonvs5• 2 months ago

“Policy” here refers to a probability distribution, i.e. a function that, given some context, assigns probabilities to possible next tokens.

This should say "...refers to a function that produces a probability distribution." The latter half of the quoted sentence describes it correctly.

sinsudo• 2 months ago

Thanks, very good explanation. One question: One could mix both kind of policies, are there hybrid policies? (with samples both from the inner and outer distributions?), if so, how are they named?

coderenegade• 2 months ago

The policy is how you select your actions -- in this case, the next token. It can be random, but it doesn't have to be. "Deterministically choose the best action" is a valid policy (we would call it the greedy policy), as long as you have some other means of injecting stochasticity so the model explores the space. Uniform random is also a valid policy, as is always selecting the same token (it obviously wouldn't be very performant, and would defeat the purpose here, but it might be fine in, for example, a multi-armed bandit scenario). Most of the time, the policy is a parameterized distribution, and we want to learn the model parameters that maximize some measure of success (the reward component).

Off-policy versus on-policy refers to what data the model is trained on. On-policy training is where the training data is collected by the policy. Off-policy training is where the data was collected by a different sampling process (e.g. we have a standard dataset that we're going to use for supervised training).

porridgeraisin2• 2 months ago

some sort of RL thing I'm too ML to understand

Oh boy.

teleforce• 2 months ago

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...

usernametaken29• 2 months ago

Very comprehensive, will give this a read, thanks man!

threecheese• 2 months ago

Did you source this from your Zotero or something like it? I love your format and resource discipline required, would be interested to know if this is “all human brain” or tool assisted.

HarHarVeryFunny2• 2 months ago

The title seems a bit misleading.

The paper is about a way to do SFT will less chance of catastrophic forgetting and performance regressions.

The idea is that SFT on new data that was NOT generated by the model (aka "off policy" data) is likely to cause problems due to the statistical mismatch between the new data and what the model has already learnt. As I understand it, their solution is to statistically align the new data with the old by feeding it to the old model, which will hopefully grok it via in-context learning, then have it regenerate it in its own words such that "off policy" data now becomes "on policy". The model can then be SFT trained on this regenerated data (i.e self-distillation).

To me SFT and "continual learning" are two distinct things.

Human/animal continual learning is always-on learning that removes the need for, and distinction between, training and inference, and it initiated by prediction failure. It's as much about skill acquisition as it is about knowledge acquisition. Continual learning can happen in any context from trying to do something (or just observing something passively) and being wrong about the outcome of your own actions, or what some external entity does next, to curiosity/boredom driven exploration and play which is more along the spectrum of pure learning with less expectation of outcomes.

Continual learning is what, one day, will let the AGI intern pick up new skills on the job by trying to do things and failing/learning/practicing until they get better. This is not the same as sending the intern home with a textbook to read, or a transcript of the conversations you had with it today, and having it take these onboard overnight, which is basically what SFT is designed to do - intermittent addition of new declarative data.

big-chungus4• 2 months ago

They don't use the old model, they use EMA of the trained model weights as teacher

HarHarVeryFunny2• 2 months ago

Yeah, and they are not self-distilling in the sense of training on the outputs of the model, but instead operating on the pre-output probability distribution - trying to minimize teacher-student differences.

Still, at the end of the day this is a type of SFT, not continual learning in the human/animal sense.

Self-Distillation Enables Continual Learning [pdf]

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