Search papers, labs, and topics across Lattice.
KRAFTON
2
2
6
3
Diversity-aware scoring transforms MoE models into dense architectures, boosting downstream accuracy by over 6% while speeding up training.
Forget RLHF's quirks: aligning LLMs is fundamentally a distribution learning problem, and preference distillation offers a theoretically sound and empirically strong alternative.
