How does the multi-turn RL framework in LongNav-R1 compare to fixed-length memory baselines on the VLN-CE benc

Abstract

Abstract: This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinfo

Research Question

How does the multi-turn RL framework in LongNav-R1 compare to fixed-length memory baselines on the VLN-CE benchmark in terms of success rate and SPL for navigation tasks with varying horizon lengths?

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