arXiv Machine Learning
Evolutionary Soups: Evolving Mixture-of-Experts for Multi-Objective LLM Alignment
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Pareto-Optimal Offline Reinforcement Learning via Smooth Tchebycheff Scalarization
arXiv:2604.13175v2 Announce Type: replace-cross Abstract: Large language models can be aligned with human preferences through offline reinforcement learning (RL) on small labeled datasets. While sing...
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Sep 10
An Evolutionary Framework for Automatic Optimization Benchmark Generation via Large Language Models
The paper introduces LLM-EBG, an evolutionary framework that uses a large language model as a generative operator to automatically create optimization benchmarks. By generating unconstrained single-objective continuous minimization problems expressed as mathematical formulas, the framework can produce benchmarks that consistently favor a target algorithm over a comparison algorithm in over 80% of trials. Landscape analysis shows that these generated problems exhibit distinct geometric traits, such as sensitivity to variable scaling, reflecting the search behaviors of different optimization methods.
arXiv Machine Learning
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HarnessCompass: Guiding Automatic Harness Evolution toward Generalizable and Effective Agent Harnesses
arXiv:2608. 01918v1 Announce Type: new Abstract: Harness design plays a critical role in agent performance by shaping how large language models (LLMs) perceive, reason over, and act within executable environments.
arXiv AI
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Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
The paper introduces Personalized Group Relative Policy Optimization (P‑GRPO), a new alignment framework for large language models that separates advantage estimation from immediate batch statistics. By normalizing advantages using preference‑group‑specific reward histories instead of the concurrent generation group, P‑GRPO maintains contrastive signals for distinct user preferences. Experiments across various tasks show that P‑GRPO converges faster and yields higher rewards than standard GRPO, improving alignment with heterogeneous human preferences while preserving general capabilities.
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Margin Adaptive DPO: Leveraging Reward Model for Granular Control in Preference Optimization
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arXiv AI
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QDEvo: A Multi-Objective Quality-Diversity Framework for Automated Heuristic Design
arXiv:2607. 11916v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) with evolutionary computation has emerged as a powerful paradigm for automated heuristic design in combinatorial optimization.