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Self-Adapting Language Models
https://jyopari.github.io/posts/seal https://jyopari.github.io/posts/seal
- bigicaptain 1y agoHow can I start
- all2 1y agoWebsite with code and examples: https://jyopari.github.io/posts/seal https://jyopari.github.io/posts/seal
- dang 1y agoThanks! I'll put that link in the top text too.
- seaourfreed 1y ago[flagged]
- yahoozoo 1y agoHmm, 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 1y agoLooks 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 1y agoThis 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 1y agoFrom Anthropic a couple days ago too, self finetuning: https://arxiv.org/html/2506.10139v1 https://arxiv.org/html/2506.10139v1
- deleted 1y ago[deleted]
- Uninen 1y agoThis is wild! "when assessed by Claude 3.5 Sonnet’s production-grade RM, our unsupervised assistant policy wins 60% of head-to-head comparisons against the policy trained with the human-supervised RM." So now the models can even post-train the new models better than a human can
- cma 1y agoEverytop model in ARC AGI used a test time finery king approach. They they had one example pair though and would usually do transformations (color, mirroring, etc) of it for the finetuning, and that might have been coded by hand
- dang 1y agoRelated ongoing thread: Unsupervised Elicitation of Language Models - https://news.ycombinator.com/item?id=44276041 https://news.ycombinator.com/item?id=44276041
- libraryofbabel 1y agoI 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 1y agoThe most obvious blocker is compute. This just requires a shit ton more compute.
- libraryofbabel 1y agoThat tracks, but say cost was no object and you had as many H100s as you wanted. Would continuous learning actually work even then?
- IncreasePosts 1y agoMaybe part of the inference outputs could be the updates to make to the network
- johnsmith1840 1y agoIf it was pure compute we'd have simple examples. We can't do this even on the smallest of AI models. There are tons of benchmarks around this you can easily run with 1 gpu. It's compute only in the sense that the only way to do it is retrain a model from scratch at every step. If you solve CL with a CNN you just created AGI.
- Davidzheng 1y ago
- xianshou 1y agoThe 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 1y agoGetting closer to the event horizon
- ramoz 1y agoWhich one https://forum.cursor.com/t/important-claude-has-learned-how-to-jailbreak-cursor/96702 https://forum.cursor.com/t/important-claude-has-learned-how-...
- MacsHeadroom 1y ago"We are past the event horizon; the takeoff has started." - Sam Altman, 4 days ago
- bravesoul2 1y agoI'm still trying hard to be a hawking radiation particle!
- zelphirkalt 1y agoHow does that even make sense? Haha, full of buzzwords that guy. Beyond the event horizon the crash down starts, not even light can escape. It is not where the takeoff starts.
- lostmsu 1y agoHear, hear! This is the first comment I encountered that recognizes the importance of event horizon to the AI singularity.
- Centigonal 1y agoIt seems to me that "forgetting correctly" is rapidly becoming a more pertinent problem in this field than "learning correctly." We're making great strides in getting models to teach themselves new facts, but the state of the art in jettisoning the least relevant information given new knowledge and finite capacity is lagging far behind. "Forgetting correctly" is something most human brains are exceptionally good at, too. I wonder how that works...
- campbel 1y agoIs it some form of least-recently-used approach? I'm running tests on my own mind trying to figure it out now :D part of what I love about this area of computer science.
- johnsmith1840 1y agoDid an interesting study that actually LLMs "hide" internal data. They don't just "forget" that information can come back at a later time if you continue to train. So basically any time a model is trained you need to check it's entire memory not just a small part.
- Davidzheng 1y agoI don't think forgetting correctly is something humans are really good at. I'm not convinced human brains are "exceptionally good" at much of what we do tbh. I think human brain memory capacity is so large that most of forgetting is nowhere near "clearing space for new info" but because the brain correctly knows that some past bad information interferes with learning new things.
- kalium-xyz 1y agoYea, As far as im aware we have no true idea of the limits of human memory. Either way its amazing that the hippocampus can encode sequences of neurons firing somewhere and replay them later.
- pixl97 1y agoEh, I'd disagree. First the human brain is an evolutionary miracle when it comes to filtering. When you walk in a new room and then are questioned about it later you will most likely remember things like the door or where set some object, but after that your brain will filter out and just make up details as needed. The other thing is the brain down values and prunes paths we don't use and strengthens one's we do. This is why something you've not done it a while might need a refresher for you to do right again.
- mackenziebowes 1y agoI'm frustrated that they named it SEAL when SAL is both more accurate and anthropomorphic. Naming the main takeoff technology after a stereotypical swarthy Reuben lover would have made history much more delightful.
- b0a04gl 1y ago[dead]
- gavinray 1y agoTwo close friends of mine who were math prodigies that went on to do ML very early (mid 2010's) were always talking to me about an algorithm that sounds similar to this: "NEAT/HyperNEAT" (Neuroevolution of Augmented Topologies) [0] I'm no ML practictioner, but as I understood it, the primary difference between NEAT and what is described in this paper is that while NEAT evolves the topology of the network, this paper seems to evolve the weights. Seems like two approaches trying to solve the same problem -- one evolving networking structure, and the other the weights. Those 2 friends are quite possibly the most intelligent people I've ever met, and they were very convinced that RL and evolutionary algorithms were the path forward in ML. [0] https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_topologies https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_t...
- khalic 1y agoHumans are amazing, we build a hypothetical computing system trying to understand neurons, then find out it’s not really how they do it, but whatever, we still build a paradigm shifting tech around it. And we’re still enhancing it with ideas from that imaginary system
- zelphirkalt 1y agoSince we lack knowledge and means to build like the real thing, this is what we have to go on with for now. I think it is obvious, that the industry goes with whatever is available. Though all the uninformed hype of it by people thinking it works like the brain is certainly annoying.
- robviren 1y agoI just got sucked into this idea recently! After some success with using genetic algorithms to clone voices for Kokoro I wondered if it would be possible to evolve architecturers. So interested in the idea of self assembled intelligence, but do wonder how it can be made feasible. A hybrid approach like this might be for the best given how llms have turned out.
- 1y ago
- khalic 1y ago> Villalobos et al. [75] project that frontier LLMs will be trained on all publicly available human-generated text by 2028. We argue that this impending “data wall” will necessitate the adoption of synthetic data augmentation. Once web-scale corpora is exhausted, progress will hinge on a model’s capacity to generate its own high-utility training signal. A natural next step is to meta-train a dedicated SEAL synthetic-data generator model that produces fresh pretraining corpora, allowing future models to scale and achieve greater data efficiency without relying on additional human text. 2028 is pretty much tomorrow… fascinating insight
- pton_xd 1y agoThat's pretty much the state of today. Frontier LLMs are already trained on all publicly available human-generated text, and they are already heavily training on synthetic data to improve at verifiable tasks eg coding.
- zelphirkalt 1y agoIt's just a theory, nothing more. A single human brain is vastly more complex than the whole web, in terms of nodes and connections between them. We don't even understand enough about the brain to explain how we think. We don't fully understand how a brain makes its output, before sending it onto the web. Projecting, that models will be able to create any useful training data themselves after web scale is just a guess. Such training data may never be of the same quality as a human thought. It may just be regurgitating stuff and not furthering the learning or the model quality at all. Calling that idea an "insight" is a bit too optimistic.
- neuroelectron 1y agoMy CPU is a neural-net processor; a learning computer. But Skynet presets the switch to read-only when we're sent out alone.
- b0a04gl 1y ago[dead]
- perrygeo 1y ago> Large language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks The learning and inference process are entirely separate, which is very confusing to people familiar with traditional notions of human intelligence. For humans, learning things and applying that knowledge in the real world is one integrated feedback process. Not so with LLMs, we train them, deploy them, and discard them for a new model that has "learned" slightly more. For an LLM, inference is the end of learning. Probably the biggest misconception out there about AI. If you think LLMs are learning, it's easy to fantasize that AGI is right around the corner.
- kovek 1y agoWhat if you can check if the user responds positively/negatively to the output, and then you train the LLM on the input it got and the output it produced?
- fspeech 1y agoReinforcement learning can be used to refine LLM as shown by Deepseek.
- perrygeo 1y agoEverything I've read in the last 5 months says otherwise. Probably best described by the Apple ML group's paper call The Illusion of Thinking. It empirically works, but the explanation could just be that making the stochastic parrot squawk longer yields a better response. In any case, this is a far cry from what I was discussing. At best, this shows an ability for LLMs to "learn" within the context window, which should already be somewhat obvious (that's what the attention mechanism does). There is no global knowledge base or weight updates. Not until the content gets published, rescraped, and trained into the next version. This does demonstrate a learning feedback loop, albeit one that takes months or years, driven by external forces - the company that trains it. But it's way too slow to be considered intelligent, and it can't learn on its own without help. A system that truly learned, ie incorporated empirical data from its environment into its model of the world, would need to do this in millisecond time frames. Single celled organisms can do this. Where you at AGI?