Make classic BM25 search smarter without rebuilding expensive neural indexes — by optimizing query rewriting one token at a time. A genuinely clever approach 🔎 Title: STORM: Stepwise Token Optimization with Reward-Guided Beam Search URL: 🔎 Overview STORM trains a query-rewriting model guided by retrieval quality. At each generated token, it scores candidate expansions against a BM25 index, concentrating exploration on the vocabulary that actually improves search. ❓ Challenges Solved Modern retrieval leans on dense and learned-sparse neural models, while lexical methods like BM25 are fast but weak on synonyms and paraphrases. ・Dense neural retrievers need expensive index rebuilds whenever the model changes ・LLM query rewriting tends to produce well-formed but retrieval-ineffective or harmful terms ・Training gives only delayed sequence-level feedback, obscuring which individual terms actually helped 💡 Methodology & Proposed Approach ・Self-supervised training via reward-guided beam search driven by retrieval performance ・At each token, candidate expansions are scored against BM25 and low performers pruned ・This turns retrieval metrics into token-level signals, focusing search on effective vocabulary ・Using BM25 indexes means no neural index rebuilding — infrastructure stays light 📊 Experimental Results ・0.6B-8B models match or exceed competitive LLM rewriters ・Maintains BM25's speed advantage ・The 8B variant rivals much larger proprietary systems ・Zero-shot transfer to 18 languages (MIRACL) beats dedicated multilingual dense retrievers on average 🌍 Use Cases It fits search stacks that want to avoid index-rebuild costs, and systems that need cheap multilingual boosts. Since it lifts performance while keeping an existing BM25 pipeline, it's an easy-to-adopt answer for teams running search in production. #Retrieval# #BM25#