Booth 21-25 | AI Data Management Zone | Tokyo Big Sight
There’s a quiet result from Cheng et al. (Renmin University and Microsoft Research, 2026) that I keep coming back to. They gave large language models access to a minimal sandbox
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
