We study zeroth-order post-training of language models by optimizing only LoRA adapter parameters with population-based black-box optimizers. Representing each candidate as a single LoRA weight vector with frozen base weights, we compare Differential Evolution (DE) and adaptive variants (JADE, SHADE), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) under a unified pipeline on the Countdown arithmetic task. To address clifflike fitness landscapes, we introduce a dense reward shaping function providing partial credit for structure, number usage, and numeric closeness. Across small models, these optimizers substantially improve reward and accuracy, with adaptive DE variants emerging as consistently strong performers.
M. Ulyanova, Fedor Buzaev, Andrei M. Ermakov· Proceedings of the Genetic a...· 0 citations
A core task for music streaming platforms is retrieving and ranking tracks in response to user queries over multi-million-track catalogs. Existing approaches either rely on tag-based, entity-centric retrieval and recommendation, which struggle with implicit and subjective queries that fall outside a predefined tag vocabulary, or on recent LLM-based generative methods that circumvent this limitation but are prone to hallucinations and factual errors. We introduce a semantic playlist generation service that retrieves and ranks tracks based on meaning rather than keyword overlap, while avoiding hallucination-related failures. Each track is represented as structured text combining metadata, lyrics, and descriptive attributes, and both user queries and track representations are encoded into a shared embedding space using an LLM. A cross-encoder reranker built on the same backbone refines candidate ranking, and its signals are distilled into the embedder to reduce serving cost. In offline and production evaluations, our semantic vector-search pipeline achieves the highest playlist quality, improving Precision@10 from 64% with faceted search and 74% with direct LLM generation to 81%, while remaining compatible with low-latency, large-scale deployment. In an online A/B test on smart-speaker traffic, routing a share of playlist requests to our system yields a consistent double-digit relative uplift in Average Time Spent, indicating that meaning-aware retrieval substantially enhances user engagement and supports broader production rollout.
Rinat Mullakhmetov, Fedor Buzaev, R. Bogachev et al.· Annual International ACM SIG...· 0 citations