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cs.LG2026
A Close Look At World Model Recovery In Supervised Fine-Tuned LLM Planners
Patrick Emami, Nan Qiang, Peter Graf
Supervised fine-tuning (SFT) improves end-to-end classical planning in large language models (LLMs), but do these models also learn to represent and reason about the planning probl…
cs.LG2018
Learning Permutations with Sinkhorn Policy Gradient
Patrick Emami, Sanjay Ranka
Many problems at the intersection of combinatorics and computer science require solving for a permutation that optimally matches, ranks, or sorts some data. These problems usually…