What is the impact of varying reflection memory length on the success rate and average reward of LLM agents us

Abstract

Abstract: This paper develops LongNav-R1, an end-to-end multi-turn reinforcement learning (RL) framework designed to optimize Visual-Language-Action (VLA) models for long-horizon navigation. Unlike existing single-turn paradigm, LongNav-R1 reformulates the navigation decision process as a continuous multi-turn conversation between the VLA policy and the embodied environment. This multi-turn RL framework offers two distinct advantages: i) it enables the agent to reason about the causal effects of historical interactions and sequential future outcomes; and ii) it allows the model to learn directly from on

Research Question

What is the impact of varying reflection memory length on the success rate and average reward of LLM agents using Reflexion on the WebShop benchmark compared to fixed-length memory baselines?

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