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> We have found that the performance of o1 consistently improves with more reinforcement learning (train-time compute) and with more time spent thinking (test-t
by modeless 2y ago
> We have found that the performance of o1 consistently improves with more reinforcement learning (train-time compute) and with more time spent thinking (test-time compute).
Wow. So we can expect scaling to continue after all. Hyperscalers feeling pretty good about their big bets right now. Jensen is smiling.
This is the most important thing. Performance today matters less than the scaling laws. I think everyone has been waiting for the next release just trying to figure out what the future will look like. This is good evidence that we are on the path to AGI.
- ffsm8 2y agoIt'd be interesting for sure if true. Gotta remember that this is a marketing post though, let's wait a few months and see if its actually true. Things are definitely interesting, wherever these techniques will get us AGI or not
- XCSme 2y agoNvidia stock go brrr...
- deleted 2y ago[deleted]
- acchow 2y agoEven when we start to plateau on direct LLM performance, we can still get significant jumps by stacking LLMs together or putting a cluster of them together.
- gizmo 2y agoMicrosoft, Google, Facebook have all said in recent weeks that they fully expect their AI datacenter spend to accelerate. They are effectively all-in on AI. Demand for nvidia chips is effectively infinite.
- seydor 2y agoUntil the first LLM that can improve itself occurs. Then $NVDA tanks
- modeless 2y agoMore, from an OpenAI employee: > I really hope people understand that this is a new paradigm: don't expect the same pace, schedule, or dynamics of pre-training era. I believe the rate of improvement on evals with our reasoning models has been the fastest in OpenAI history. > It's going to be a wild year. https://x.com/willdepue/status/1834294935497179633 https://x.com/willdepue/status/1834294935497179633