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Ah, I remember reading this paper! Essentially, they created synthetic data by solving search problems using A*. Trained on this data, transformers unsurprising
by a_wild_dandan 2y ago
Ah, I remember reading this paper! Essentially, they created synthetic data by solving search problems using A*. Trained on this data, transformers unsurprisingly learned to solve these problems. They then improved their synthetic data by repeatedly solving a given problem many times with A*, and keeping only the shortest step solution. Transformers learned to be competitive with this improved search heuristic too!
Pretty remarkable stuff. Given the obsession over chat bots, folk often miss how revolutionary transformer sequence modeling is to...well, any sequence application that isn't a chat bot. Looking solely at speeding up scientific simulations by ~10x, it's a watershed moment for humanity. When you include the vast space of other applications, we're in for one wild ride, y'all.
- jadbox 2y agoRemarkable indeed, I've been toying with many smaller, unorthodox use-cases for LLMs and continue to be surprised.
- smcin 2y agoI want to see an optimal (2P) speedrun with Sonic the Hedgehog and Tails. As a fun proof. There are two players working together; also it has more degrees-of-freedom than a Sokoban.
- jasonjmcghee 2y agoCan you say more? Where can I read about some of these alternate use cases?
- ebri 2y agoyes I want to learn about this too! Hadn't thought about it like that.
- littlestymaar 2y agoIt uses transformers but it's not an llm though.
- unraveller 2y agowhat are those other applications? so now everyone has SoTA warehouse scheduling in their pockets no one can be expected to do anything or go anywhere inefficiently.
- deleted 2y ago[deleted]
- electricships 2y agoAfter AlphaZero, people tried to train various combinatorial solvers IIRC, they were unable to exceed Sota of SAT solvers (which admittedly are the results of decades of research) maybe we just needed bigger networks, or transformers, or just more training compute
- radomir_cernoch 2y agoI'd love to read more. Do you know any sources?
- algo_trader 2y agoTry Reinforcement Learning for Combinatorial Optimization: A Survey or a more recent and spirited discussion on reddit.. https://old.reddit.com/r/reinforcementlearning/comments/196idl8/reinforcement_learning_for_optimization/khw3ox5/ https://old.reddit.com/r/reinforcementlearning/comments/196i...
- nickpsecurity 2y agoLink for those who need it: https://arxiv.org/abs/2003.03600 https://arxiv.org/abs/2003.03600
- davidguetta 2y agoMm theres one purely based on position analysis and not tree search that has GM level in chess. From deepmind
- lou1306 2y agoSAT appears to be rather impervious to statistical methods because it is very non-linear and non-local (besides, of course, being NP-complete). Flip one single literal anywhere and poof!, your formula mght go from SAT to UNSAT, or vice versa, or you might enable a ton of simplifications, etc. So it might be very hard to train a network on such a riotous state space. Additionally, in practice SAT solvers are also supposed to provide witnesses (i.e., a satisfying assignment or an unsatisfiability core) to back their verdicts, and that may be an ever taller order for ML.