
Imagine if your robot’s “eyes” were only right 85% of the time.
Imagine if every time a human hand reached into a dark shelf or pinched a small component, the robot’s vision system simply guessed where the fingers went. This is the
Booth 21-25 | AI Data Management Zone | Tokyo Big Sight

Imagine if every time a human hand reached into a dark shelf or pinched a small component, the robot’s vision system simply guessed where the fingers went. This is the

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 this model been properly fine-tuned on examples of the behavior you want? Supervised Fine-Tuning (SFT) sits between pre-training and the
