The paper introduces CAP (Candidate Acquisition Policy), a method that learns a query policy from sparse historical records of related optimization tasks. CAP models shared structure and task-specific variation to generate interactive practice functions, then trains a feedback-conditioned policy that operates under a fixed evaluation budget. Experiments on synthetic task families and engineering simulation benchmarks (BOPTEST and SU2) show CAP achieving the lowest median final-budget values compared to other methods.
arXiv:2510. 12624v2 Announce Type: replace-cross Abstract: Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire.
arXiv:2608. 19790v1 Announce Type: new Abstract: Discovering materials with desirable properties often requires searching large candidate spaces while experimental or computational evaluations remain costly.
The paper introduces Strategy Accumulation and Guided Execution (SAGE), a two-stage framework that makes automated fine-tuning of large language models cumulative. In the first stage, a multi-agent pipeline uses Monte Carlo Tree Search to explore training strategies while a Distillation Agent records task-specific insights and cross-task confidence scores into a structured repository. In the second stage, SAGE retrieves relevant experience from this repository to guide training on new tasks, achieving a 12.4‑percentage‑point improvement over a baseline pipeline without accumulated experience on nine unseen tasks.
arXiv:2610.14913v1 Announce Type: new Abstract: Reinforcement learning (RL) is essential for adaptive decision-making, e.g., robust Meta-RL and domain randomization (DR), and efficient post-training...
arXiv:2610.07045v1 Announce Type: cross Abstract: Many scientific studies allow costly auxiliary information to be collected during data labeling but not at deployment. Examples include diagnostic te...
arXiv:2607. 17632v1 Announce Type: cross Abstract: Time series data play a pivotal role across numerous domains, including healthcare and manufacturing.
arXiv:2606. 08854v1 Announce Type: cross Abstract: Standard Reinforcement Learning with Verifiable Rewards (RLVR) training allocates a fixed rollout budget to every query, without regard for what each query's difficulty means for the current policy.
arXiv:2602. 17976v2 Announce Type: replace-cross Abstract: In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to identify an unknown ground-truth hypothesis with as few queries as possible.
arXiv:2607. 29606v1 Announce Type: cross Abstract: A fundamental challenge of vector search is achieving consistently high recall while minimizing computational costs.
arXiv:2606. 08601v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting.
Goldilocks RL proposes an adaptive data‑selection strategy for reinforcement learning in language models, using a Selector network to predict reward variability and prioritize questions that are neither too easy nor too hard. By continuously adapting to the model’s evolving abilities, Goldilocks improves training efficiency on large‑scale reasoning datasets, achieving up to 78% fewer optimization steps compared to standard GRPO. The approach addresses the sample‑inefficiency problem of sparse rewards in reasoning tasks.
arXiv:2608. 10108v1 Announce Type: new Abstract: Long-horizon agents accumulate trajectories spanning hundreds of interleaved reasoning, action, and observation steps, where answering a query may depend on evidence buried far back in the history.