7 ms·
Solving a million-step LLM task with zero errors
- LMKIIW 11mo agoI dunno, even though the authors address its use, making the task Tower of Hanoi doesn't meet the excitement of the title.
- NitpickLawyer 11mo agoAnd yet the reverse paper was posted ad nauseam, covered by every news slop site, and overblown with really negative takes.
- charcircuit 11mo agoEspecially since it's a recursive problem so each step is naturally broken up into subtasks. And the algorithm of what subtasks to break it up in to is public. This makes it much easier for it to get down to a case that the LLM can reliable solve.
- RossBencina 11mo agoI guess that the subtask decomposition of many (sub)problems is known and in the training distribution. How many real-world problems are resistant to divide-and-conquer? Presumably most/all of the unsolved mathematics conjectures. What else?
- cs702 11mo agoNice! Briefly, the idea is recursively to decompose tasks into the simplest possible steps, recursively call (relatively small) LLMs as agents to execute one step at a time, and using a clever voting scheme to choose how to execute each step. The authors use this technique to get a relatively small LLM to solve Towers of Hanoi with 20 rings (1M steps). All of it using natural language. The most obvious question is whether other tasks, more interesting -- less "rote" -- than Towers of Hanoi, can similarly be recursively decomposed into simple steps. I'm not sure that's always possible.
- adastra22 11mo agoWhy not? That's basically how NASA manages large projects.
- mulmen 11mo agoNASA has done a lot of amazing things but I wouldn’t bet on them winning a Super Bowl.
- HarHarVeryFunny 11mo agoThey'd have a 50% chance of winning one on Mars, since it would just be NASA vs China
- bangaladore 11mo agoEvery year NASA has a 50% chance of winning the Superbowl- even on Earth! Either they win or don't. /s
- Julien_r2 11mo agoI could imagine that even a small task at NASA might involve more knowledge and logic than the smallest task for a Hanoi's tower problem. Depends on what is considered as small enough for the LLM to be resolved with a high confidence.
- Uehreka 11mo agoOne issue I often run into with this stuff is the tightly coupled nature of things in the real world. I’ll fashion an example: Let’s say you break a job down into 3 tasks: A, B and C. Doing one of those tasks is too much for an LLM to accomplish in one turn (this is something you learn intuitively through experience), but an LLM could break each task into 3 subtasks. So you do that, and start by having the LLM break task A into subtasks A1, A2 and A3. And B into B1, B2 and B3. But when you break down task C, the LLM (which needs to start with a fresh context each time since each “breakdown” uses 60-70% of the context) doesn’t know the details of task A, and thus writes a prompt for C1 that is incompatible with “the world where A1 has been completed”. This sort of “tunnel vision” is currently an issue with scaling 2025 agents. As useful context lengths get longer it’ll get easier, but figuring out how to pack exactly the right info into a context is tough, especially when the tool you’d reach for to automate it (LLMs) are the same tool that suffers from these context limitations. None of this means big things aren’t possible, just that the fussyness of these systems increases with the size of the task, and that fussyness leads to more requirements of “human review” in the process.
- zer00eyz 11mo agoOn the surface this is an interesting concept... The paper however, meh... No mention of MoE. One would think this is a logical evolution of that but not a mention (that I saw). Its own rubric for the task, Towers of Hanoi, was admittedly weak. LLM papers are starting to look like the last decade of JS frameworks and Tools. Only with less code and more academics, and thats disappointing, because I think a lack of pragmatism and grounding is now holding the field back...
- awei 11mo agoone issue I see is when steps in a plan depend on one another, when you cannot know all the next steps exactly before seeing the results of the previous ones, when you may have to backtrack sometimes
- beefnugs 11mo agoThis is actually good insight and worded in a simple way that clicked in my brain, thanks!
- htrp 11mo ago> The approach relies on an extreme decomposition of a task into subtasks, each of which can be tackled by focused microagents. The high level of modularity resulting from the decomposition allows error correction to be applied at each step through an efficient multi-agent voting scheme. Big if that the decomposition and the voting happen accurately for anything other than toy problems
- yorwba 11mo agoThe approach in the paper specifically addresses the case where an LLM can usually solve a task when it requires few steps, but fails for the same kind of task with more steps because it randomly gets a step in the middle wrong and then derails. It can't do anything for tasks that the LLM can't solve even when there's just a few steps. In other words, it compensates for random error, not systematic error.
- andai 11mo agoI have ADHD and the same approach works for me. (In fact, most days it is essential!)
- binary132 11mo agodo you have an algorithm for breaking down, organizing, and scheduling the small tasks, though? can it also be broken down?
- andai 11mo agoWorth opening the pdf just for the graph on page 1.
- mNovak 11mo agoReally seems like the reason logarithmic scales were invented..
- arscan 11mo agoIn case you want to know what’s going on in the left side of that chart, they gave a log scale in appendix a. I was thinking it was silly to not just use that version on the top, but I guess log scales make big differences ’feel’ smaller.
- kevmo314 11mo agoAt that rate, they might as well have gone one step further and made the x axis exponential scale to make it feel even bigger.
- levocardia 11mo agoA log scale is actually appropriate in this context from a first-principles perspective. Per scaling laws (and also general behavior of epsilon-probability of failure multiplied N times), you would generally expect more vs. less effective techniques to have multiplicatively greater or fewer steps until failure, not additively greater/fewer. Figure 1 is comical, but the appendix figure is the more scientifically appropriate one.
- esafak 11mo agoA striking example of how not to present data. If the Cognizant AI team is here: please can you fix it in the next version of the paper?
- rdlw 11mo agoI think it's a brilliant example of how to use data to make a point. https://xkcd.com/1162/ https://xkcd.com/1162/
- mattpk 11mo agoHere is the pseudocode of MAKER: state = init_state() while state is not complete: state = LLM("You are a helpful assistant. The rules and format of the game is [...]. The correct strategy to use at each step is [...]. The current state is [...]. Output the state after making the next move")
- vatsachak 11mo agoAnd you can decompose the proof of Fermat's last theorem into logical combinators. The meat is in decomposing the difficult problem into steps
- sublimefire 11mo agoThe problem is how to even define a task using the English language and make sure there is enough entropy to infer the detailed intent. For it to be later split into zillions of small steps which can be executed over time by an LLM.
- gridspy 11mo agoIn English, that's hard, but there are programming languages ... specialized in breaking a complex task down for computers to understand. ...
- lubujackson 11mo agoThis has seemed to me to be the natural next step to turn LLMs into more deterministic tools. Pushing the frontier is nice, but I think LLMs have a whole different gear when they are able to self-decompose in a reliable way. Most of my success creating reusable LLM products came from determining where requirements/outputs need to be "hard" vs. "soft".
- robot-wrangler 11mo ago> However, this result made it clear that the reliability of state-of-the-art LLMs is fundamentally limited: If they need to complete every step correctly in order to solve a task, after a certain number of steps they will almost surely fail as a result of an underlying propensity to make errors, even when the answer should be obvious. While an error rate of 1-in-1,000 seems low, and would be great on a traditional LLM benchmark, on a task that requires successful execution of thousands of steps in a row, such a system results in inevitable failure. What a relief to see an obvious problem actually acknowledged. I can't even guess how many times I've been shouted down about this exact topic in the reasoning debates on HN, or seen papers just kind of glossing over it as if it were a non-issue. The next really natural question is.. if you're committed to decomposing a problem into tons of microsteps and voting.. why aren't we just embracing hybrid symbolic systems? The decomposition step kind of implies you're in a problem domain where variables separate out somewhat cleanly and that this should be doable. As far as I can tell the "voting" discussed in the paper is about candidate outputs, i.e. solutions to subproblems? If you switch to hybrid symbolic systems, then you can vote on candidate inputs to solvers and at least be damned sure that their output is always correct. Also the success of chain-of-code compared with chain-of-thought approaches could actually imply that having no real solver is maybe not the obstacle you'd expect! Maybe you can invent a semiformal logic just in time that appears to be expressive enough to encapsulate the problem domain, and have the LLM emulate a nonexistent solver. If the error rate with this sort of approach is still too high, then at least you know concretely what solver or formal-language you need to implement in order to improve.
- amelius 11mo ago> While an error rate of 1-in-1,000 seems low, [...], on a task that requires successful execution of thousands of steps in a row, such a system results in inevitable failure. This is also why (edit: non-LIDAR) FSD cars are an illusion.
- JimDabell 11mo agoWaymo has driven a hundred million miles and is far safer than human drivers.
- DeathArrow 11mo agoSome real life problems cannot be decomposed or cannot be decomposed with ease by an LLM. Also, if we decompose a big task into many tasks, some might be solved in an incompatible way with the rest of the tasks and you can not combine them.
- keks8430 11mo agoRight, it’s kind of like solving systems of linear equations. Some can be solved just by reordering, but most need you to handle all the constraints at once.
- vicentwu 11mo agoHmm... The key is to successfully decompose a big, hard problem into easier atomic sub-problems. However, the decomposition process itself is difficult, and this paper is not about that. They decompose a task using a human-written prompt.