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
3 Pith papers cite this work. Polarity classification is still indexing.
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RTPurbo converts full-attention LLMs to sparse attention by retaining full KV for retrieval heads and using a low-dimensional dynamic indexer, achieving near-lossless accuracy after minimal adaptation.
Children and LLM agents show parallel adaptations to evidence reliability in a Bayesian program induction task but differ in information-seeking costs and compliance.
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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.
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Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps
RTPurbo converts full-attention LLMs to sparse attention by retaining full KV for retrieval heads and using a low-dimensional dynamic indexer, achieving near-lossless accuracy after minimal adaptation.
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Hypothesis Generation and Inductive Inference in Children and Language Models
Children and LLM agents show parallel adaptations to evidence reliability in a Bayesian program induction task but differ in information-seeking costs and compliance.