RecRSI: ranking-scored short trials cut wasted retraining in LLM-driven recommender self-improvement
_reachsumit · x · 2026-10-30
RecRSI tackles two problems in LLM-agent recursive self-improvement of recommenders: ranking feedback is delayed until full retraining, and pre-retraining signals are unreliable. It generates diverse candidate training edits from a model diagnosis, gives each a short trial scored by full-ranking NDCG@10 on held-out probe users, and fully retrains only the best candidate. Across two sequential recommenders and three datasets it significantly beats existing self-evolving agents at equal retraining budget.
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