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
The “more data wins” era is mostly over. What’s replacing it is messier and more interesting: a stack of techniques — fine-tuning, preference optimization, retrieval — each with its own cost curve, its own failure modes, and its own demands on data quality. Pick the wrong lever and you can
