Search-Aware Reinforcement Learning for Multi-Component Query Understanding in Roblox Game Search
Paper recorded by Signals 4 on 2026-09-24 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-24 on arXiv · recorded by Signals 4 on 2026-09-25
Category: cs.AI · 人工智能 · first seen 2026-09-25
Abstract
Query understanding (QU) plays a critical role in production search systems, translating raw user queries into search execution plans that drive downstream retrieval and ranking. While large language models (LLMs) have enabled QU to be framed as a structured multi-task generation problem (e.g., intent classification, query expansion), optimizing such models to produce search-engine-coupled outputs
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Cite this page: Search-Aware Reinforcement Learning for Multi-Component Query Understanding in Roblox Game Search: the #17 most recent of 400 cs.AI papers we have recorded (as of 2026-09-24). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/search-aware-reinforcement-learning-for-multi-component-query-understanding-in-r.html
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