Search-Aware Reinforcement Learning for Multi-Component Query Understanding in Roblox Game Search
A search-aware reinforcement learning framework for query understanding in Roblox Game Search improves search quality by 8.9 points over SFT policy.
A search-aware reinforcement learning framework for multi-component query understanding in Roblox Game Search improves search quality by 8.9 points over supervised fine-tuning, demonstrating component-specific optimization for query understanding.
- A search-aware reinforcement learning framework improves QU for Roblox search, raising NDCG@20 by 8.9 points over SFT.
- Products
- Roblox Game Search
- Organizations
- Roblox
- Countries
- Roblox
- AI models
- Large language models (LLMs)
From the original article
1 min read at arxiv.orgQuery 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.
By Nayoung Choi · arxiv.org
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