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The Road to a Billion-Token Context
- schnitzelstoat 6mo agoIs such a large context window even desirable? It seems like it would consume an awful lot of tokens and, unless one was very careful to curate the context, could even result in worse performance.
- withinboredom 6mo agoFor larger codebases ... maybe it will cut down on "let me create a random number wrapper for the 15th time" type problems.
- Weryj 6mo agoYou should already have skills which mention these utilities. But maybe that’s enough tokens to feed an entire lifetime of user behaviour in for the digital twin dystopia?
- withinboredom 6mo ago"type problems" was doing the heavy lifting there, not literally "this utility".
- AureliusMA 6mo agoI remember when a large context was 8k! Nowadays that would seem extremely small, because we have new use-cases that require much larger context sizes. Maybe in the future, we will invent ways to use inference on very large contexts that we cannot even imagine today.
- faangguyindia 6mo agoimagine if you were making a database software and u could fit source code of all existing databases and their github issues in context.
- AntiUSAbah 6mo agoThats either the R&D part of this chip or Nvidia has the use case. Nvidia uses ML for finetuning and architecturing their chips. this might be one use case. Another one would be to put EVERYTHING from your company into this context window. It would be easier to create 'THE' model for every company or person. It might also be saver than having a model train with your data because you don't have a model with all your data, only memory.
- vessenes 6mo agoYes. That is, it is if you imagine a magically good self attention mechanism that could decide what in the context to attend to at any one moment. Then it would be like working with a polymath that has incredible memory. Or bringing in that aged but still senior Chief of Staff of a large company that knows where every body was buried, and why every decision was made at the time it was made, or a professor of film that has seen and can remember thousands of films. Shockingly, we seem to have found a self attention mechanism of that quality, it just has the sad property of growing at O(N^2) where N is the context length.
- alansaber 6mo agoYes because scaling tends to pay off unexpectedly
- __alexs 6mo agoDoes having 1 billion tokens mean more total tokens in the context window are actually good quality, or do we just get more dumb tokens?
- RugnirViking 6mo agothe article is almost entirely about this, yes. Current approaches require fancy tricks to fit tokens into memory, and spread attention thinner over larger numbers of tokens. The new approach tries to find a way to keep everything in a single shared memory, and process the tokens in parallel using multiple GPUs
- AureliusMA 6mo agoHow large would a 1 billion token kv even be ?!
- AntiUSAbah 6mo ago30TB for 4 bit, 60tb for 8bit res
- Schlagbohrer 6mo agoWhat does this mean: "In addition, because most AI models are not trained uniformly across their maximum context length, their reasoning quality tends to degrade gradually near the limit rather than fail abruptly." Models aren't trained across their context, their context is their short term memory at runtime, right? Nothing to do with training. They are trained on a static dataset.
- smallerize 6mo agoI think it means most of the training data is short. And a lot of the long-context examples are conversations where the middle turns are less important.
- andai 6mo agoNot sure how it is now, but a while back most of the training data was short interactions. I noticed that the longer a chat gets, the more unpredictable the models behavior becomes (and I think that's still a common jailbreak technique too). (I think it might also have something to do with RoPE, but that's beyond me.)
- AntiUSAbah 6mo agoSo for the context to work well, you need some attention mechanism which makes sure that details are not getting lost due to context amount. or lets say it differently: The LLM gets trained on static data but also on the capability of handling context in itself. Kimi introduced this https://github.com/MoonshotAI/Attention-Residuals https://github.com/MoonshotAI/Attention-Residuals but i'm pretty sure closed labs like Google had something like this for a while.
- yorwba 6mo agoThe attention residuals paper uses attention across layers for the same token, in addition to the usual case of attention across tokens within the same layer, but it doesn't do anything to address the "lost in too much context" problem. At least the number of layers is currently still low enough that there's probably no equivalent "lost in too many layers" problem yet.
- Schlagbohrer 6mo agoAmazing that they are trying to solve this with hardware rather than with a new software architecture but I suppose the current technology underlying LLM software must be far and away the best theoretically or most established, or the time taken to seek a new model isn't worth it for the big companies. I know Yann LeCun is trying to do a completely different architecture and I think that's expected to take 2-3 years before showing commercial results, right? Is that why they're finding it quicker to change the hardware?
- AntiUSAbah 6mo agoNvidia has so much money, it would be a waste if they wouldn't attack current problems on multiply points at once. People, Researcher, Investor etc. probably also want to see what would be possible and someone has to do it. I can also imagine, that an inferencing optimized system like this could split the context for different requests if it doesn't need to use the full context. Could also be that they have internal use cases which require this amount of context.
- sandworm101 6mo ago[dead]
- aurareturn 6mo agoIt is both a software and hardware problem. Software because you can train LLMs that get better at very large contexts. Hardware because no matter what you do in software, you still need faster and bigger chips. Yann LeCunn has been very wrong in the past about LLMs.[0] The approach he wants to take is to train using sensor data in the physical world. I think it's going to fail because there's near infinite amount of physical data down to Schrodinger's equation on how particles behave. There's too much signal to noise. My guess is that they'll need magnitudes more compute to even get something useful but they do not have more compute than OpenAI and Anthropic. In other words, I think LLMs will generate revenue as a stepping stone for OpenAI and Anthropic such that they will be the ones who will ultimately train the AI that LeCunn dreams of. [0]https://old.reddit.com/r/LovingAI/comments/1qvgc98/yann_lecun_says_basing_agentic_systems_on_the/o3kgyw4/ https://old.reddit.com/r/LovingAI/comments/1qvgc98/yann_lecu...
- alexreysa 6mo ago[flagged]
- Havoc 6mo agoHaving it would be useful but I'd say long before you get there one should think about structuring the data in a more meaningful sense. Breaking tasks out into subagents etc.
- Schiendelman 6mo agoMost harnesses already do this!
- stephschie 6mo agoHmm, I'm not convinced that is the direction we want to go in. It's not like we have all the context of everything we ever learned present when making decisions. Heck, even for CPUs and GPUs we have strict hierachy of L1,L2,L3 shared, caches to larger memory units with constant management of those. Feel free to surprise me, but I believe having a similar stack for LLMs is the better way to go where we will have short-term memory (system-prompt, prompt, task), mid-term memory (session-knowledge, preferences), long-term memory (project knowledge, tech/stack insights), intuition memory (stemming from language, physics, rules). But right now we haven't developed best-practices yet of what information should go into what layer at what times. Increasing the overall context window is nice, but IMHO won't help us much.
- lumost 6mo agoCurrently, it is difficult to live update the model’s parameters in response to new information. This difficulty applies at both an infrastructural level and an optimization level. We simply don’t know how to incorporate new information without losing old capabilities reliably. Pans handle this through extensive evaluation, heuristics, and experience. What we do know is that models can adapt to their context, and extending the context window is an infrastructure and capex problem first. A billion useful tokens would obviate the need for any out of band memory structures.
- wat10000 6mo agoI definitely see why effort is being put into this. But it seems inherently limiting. It's like having someone sit down in a library each day with a notebook containing all their prior work, none of which they can actually remember. At the end of the day, they write out their notes, then go home and get their memory wiped for the next day. Making that notebook longer is an obvious way to improve the system, but it seems like it's going to bump into fundamental limits.
- lumost 6mo agoAre we really any different? :) you could easily extend the above story to consolidated images/video/3d environments etc.
- tuo-lei 6mo ago[dead]
- whateveracct 6mo agospeedrunning the bitter lesson, i see
- kk_mors 6mo ago[flagged]