STOMP extends direct preference optimization to the multi-objective setting via smooth Tchebysheff scalarization and standardization of observed rewards, achieving highest hypervolume in eight of nine protein engineering evaluations.
ISBN 9798331314385
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
RTPurbo exploits intrinsic sparsity in full-attention LLMs to achieve near-lossless sparse inference after only a few hundred training steps via retrieval-head identification and a lightweight token indexer.
citing papers explorer
-
Pareto-Optimal Offline Reinforcement Learning via Smooth Tchebysheff Scalarization
STOMP extends direct preference optimization to the multi-objective setting via smooth Tchebysheff scalarization and standardization of observed rewards, achieving highest hypervolume in eight of nine protein engineering evaluations.
-
Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps
RTPurbo exploits intrinsic sparsity in full-attention LLMs to achieve near-lossless sparse inference after only a few hundred training steps via retrieval-head identification and a lightweight token indexer.