Jul 2026· GECCO Companion· pp. 237-240· 0 citations· 14 references
Computer Science
TL;DR
To address clifflike fitness landscapes, a dense reward shaping function providing partial credit for structure, number usage, and numeric closeness is introduced to address clifflike fitness landscapes.
Abstract
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.
CoPES is introduced, a cooperative coevolutionary method that decomposes the full parameter space into lower-dimensional subspaces and searches over them cooperatively to improve optimization efficiency and demonstrate an improved trade-off between memory requirements and training time for agentic LLM post-training und...
Zhiyuan Wang, Sheng-Cai Liu, Jiahao Wu et al.· 2 citations
Evolutionary Soups, a mixture-of-experts framework for fine-grained generation control, with gating networks trained via an evolutionary algorithm, achieves the best hypervolume, linear utility, and Tchebyshev utility among controllable methods on all tasks.
Lingxiao Kong, Steffen Staab, Cong Yang et al.· 0 citations
Parameter-efficient fine-tuning (PEFT) methods adapt foundation models to specific domains by selectively updating only a small subset of critical parameters, significantly reducing computational costs. Among these methods, Sparse Low-Rank Adaptation (SoRA) has emerged as an effective approach for fine-tuning large lan...
This study proposes a Large Language Model-Driven Differential Evolution (LLMDE) algorithm to reduce the reliance on handcrafted hyperparameter design. The proposed algorithm leverages a prompt engineering strategy, allowing large language models (LLMs) to dynamically select mutation strategies and configure control pa...
Rong-Mu Chai, V. Snášel, Xiao-Peng Wang et al.· 0 citations
This survey organizes recent optimizers and training optimization methods along four largely independent axes: temporal estimation, update geometry, horizon management, and representation and systems.
Large Language Model (LLM)-based applications are increasingly deployed across domains including customer service, education, and mobility. These systems are prone to inaccurate, fictitious, or harmful responses, and their vast, high-dimensional input space makes systematic testing particularly challenging. In this pap...
Lev Sorokin, Ivan Vasilev, Ken E. Friedl et al.· 0 citations
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