Systematic framework for selecting trajectories in data augmentation evaluated across five strategies
A thesis-derived arXiv preprint proposes a framework for evaluating five trajectory selection strategies—Outlierness, Diversity, Representativeness, Uncertainty, and Random—for data augmentation in spatio-temporal ML tasks. The study tests these strategies across four datasets spanning animal behavior, maritime, and urban traffic domains using linear and non-linear models with Optuna-based hyperparameter optimization. Key findings show systematic strategies (especially Outlierness and Uncertainty) outperform random selection in sparse datasets but can degrade performance in dense, high-quality datasets, with UMAP visualization confirming topological effects.
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Vector Policy Optimization (VPO) is a new RL post-training algorithm for LLMs that replaces the scalar reward paradigm with vector-valued rewards, explicitly training models to produce diverse solution sets that specialize across different reward trade-offs. VPO is designed as a near-drop-in replacement for the GRPO advantage estimator and targets inference-scaling search procedures like AlphaEvolve. Across four tasks, VPO matches or outperforms scalar RL baselines on pass@k and best@k metrics, with advantages growing as search budget increases, and unlocks evolutionary search problems that GRPO-trained models cannot solve. The paper argues that diversity-optimized post-training may need to become the default as inference-time search becomes standard.
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Evolution Strategies as a Scalable Alternative to Reinforcement Learning
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