Booth 21-25 | AI Data Management Zone | Tokyo Big Sight
Before you reach for RLHF, before you design a reward model, before you start thinking about reinforcement learning from verifiable rewards — there’s a more fundamental question worth asking: has
Speed benchmarks in AI research can feel detached from anything practical. A paper claims a 10x speedup and you skim past it because the benchmarks never quite match the thing you’re actually trying to do. The FlexGNN result — up to 95.5x faster training on large graphs — is worth
