[HN Gopher] Self-Adapting Language Models
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Self-Adapting Language Models
https://jyopari.github.io/posts/seal
Author : archon1410
Score : 67 points
Date : 2025-06-13 19:03 UTC (3 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| all2 wrote:
| Website with code and examples:
| https://jyopari.github.io/posts/seal
| dang wrote:
| Thanks! I'll put that link in the top text too.
| yahoozoo wrote:
| Hmm, it looks like it's just a framework that fine-tunes LoRA
| adapter then merges the adapter into the original model. It is
| using the PeftModel and its "merge_and_unload" from the
| HuggingFace library which performs the adapter merge into the
| base model...what is new here, exactly?
| observationist wrote:
| Looks like it may be the stability of the approach, avoiding
| alignment tax and model collapse.
|
| I'd love to see a full circle of hypernetworks, with both
| models continuously updated through generated LoRAs, the
| hypernetwork updated to accommodate the new model state. You'd
| need a meta-hypernetwork to apply LoRAs to the hypernetwork,
| and then you could effectively have continuous learning.
| ivape wrote:
| This still relies on fine-tuning. How would a cloud LLM deal with
| this if every user literally fine tunes it? Seems like something
| destined for local private LLMs, but the notion of continuous
| fine tuning locally at the moment is sci-fi level stuff because
| the hardware is just not there yet (we can barely inference well
| with a reasonable sized context).
| cma wrote:
| From Anthropic a couple days ago too, self finetuning:
|
| https://arxiv.org/html/2506.10139v1
| libraryofbabel wrote:
| I wonder if anyone who's really _in the know_ could summarize
| where the research is at with getting LLMs to learn "on the job"
| (through continuous fine tuning or whatever) and what the
| blockers are to this being a useful deployable thing, e.g. having
| a model+coding agent that can actually learn a codebase over time
| (cost? model collapse? something else?).
|
| I'm sure this is something the big labs are trying but from the
| outside as a user of LLMs it feels like people don't talk about
| this very much and instead the focus right now is on better
| training (eg reinforcement learning) with the assumption that
| anything else not learned during training will be stuffed into
| the context somehow as needed. But from a naive perspective the
| lack of learning from experience after training seems like the
| biggest thing standing between us and AGI.
| ivape wrote:
| The most obvious blocker is compute. This just requires a shit
| ton more compute.
| libraryofbabel wrote:
| That tracks, but say cost was no object and you had as many
| H100s as you wanted. Would continuous learning actually
| _work_ even then?
| IncreasePosts wrote:
| Maybe part of the inference outputs could be the updates to
| make to the network
| kadushka wrote:
| The most obvious blocker is catastrophic forgetting.
| free_bip wrote:
| The most obvious problem is alignment. LLM finetuning is
| already known to be able to get rid of alignment, so any form
| of continuous fine tuning would in theory be able to as well.
| notnullorvoid wrote:
| What kind of alignment are you referring to? Of course more
| fine-tuning can disrupt earlier fine-tuning, but that's a
| feature not a bug.
| mnahkies wrote:
| I'm no expert, but I'd imagine privacy plays (or should play) a
| big role in this. I'd expect that compute costs mean any
| learning would have to be in aggregate rather than specific to
| the user which would then risk leaking information across
| sessions very likely.
|
| I completely agree that figuring out a safe way to continually
| train feels like the biggest blocker to AGI
| xianshou wrote:
| The self-edit approach is clever - using RL to optimize how
| models restructure information for their own learning. The key
| insight is that different representations work better for
| different types of knowledge, just like how humans take notes
| differently for math vs history.
|
| Two things that stand out:
|
| - The knowledge incorporation results (47% vs 46.3% with GPT-4.1
| data, both much higher than the small-model baseline) show the
| model does discover better training formats, not just more data.
| Though the catastrophic forgetting problem remains unsolved, and
| it's not completely clear whether data diversity is improved.
|
| - The computational overhead is brutal - 30-45 seconds per reward
| evaluation makes this impractical for most use cases. But for
| high-value document processing where you really need optimal
| retention, it could be worth it.
|
| The restriction to tasks with explicit evaluation metrics is the
| main limitation. You need ground truth Q&A pairs or test cases to
| compute rewards. Still, for domains like technical documentation
| or educational content where you can generate evaluations, this
| could significantly improve how we process new information.
|
| Feels like an important step toward models that can adapt their
| own learning strategies, even if we're not quite at the
| "continuously self-improving agent" stage yet.
| bravesoul2 wrote:
| Getting closer to the event horizon
| ramoz wrote:
| Which one
|
| https://forum.cursor.com/t/important-claude-has-learned-how-...
| bigicaptain wrote:
| How can I start
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