This resolves YES if: There is a contiguous period of 12 months during which no new language model is credibly known to exist that sets a new record for most compute used during the entire training process from start to finish.

• The definition of LM is intended to include models that use other modalities or do other things in addition to language (images, RL, etc).

• This specifically does not take into account algorithmic innovations. A 10x effective compute improvement from better algorithms/utilization does not count as a 10x compute usage increase. This includes low level optimizations, or innovations that use lower precision (i.e I consider 2 FP16 FLOPs as equivalent to 1 FP32 FLOP)

• This market is conditional on it being generally understood that SOTA LMs are still being publicized, and their compute usages are at least roughly estimatable (except for i.e military). Compute usages don't have to be exact or official as long as they are credibly estimatable from public information (i.e looking at power consumption, financial reports, satellite imagery of datacenters). This market resolves N/A if compute numbers stop being estimatable in such a way that it becomes controversial as to whether models are continually using more compute.

• A fine tune of an existing LM counts the compute usage of the base LM plus the fine tuning compute; however, to qualify for this market it has to use at least 50% new compute over the last LM that qualified for this market; this is intended to exclude a SOTA LM being continually fine tuned on new data with trivial amounts of compute from technically continually setting new SOTAs on this market.

• As a sanity check, the new LM should not be substantially worse than previous compute-SOTA models on most major benchmarks where the models are fairly comparable. This is intended to exclude models which are trained with much more inefficient techniques/poorly chosen hparams that waste lots of the compute.