Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 366-377· 0 citations· 15 references
Abstract
Traditional automated feature engineering (AFE) minimizes human intervention but often neglects semantic relationships among original features, resulting in redundant or uninterpretable transformations. While recent methods leverage the domain knowledge and reasoning capabilities of Large Language Models (LLMs), they typically constrain the search space via predefined mathematical operators or suffer from poor exploration-exploitation balance due to exclusive reliance on validation feedback. To address these limitations, we propose MORE-FE, an evolutionary AFE framework that integrates multi-operator exploration with Reinforcement Learning with Verifiable Rewards (RLVR). MORE-FE treats feature transformation programs as hypotheses and evolves them using evolutionary operators that explore semantically coherent and logically structured features through various prompting strategies. Moreover, it employs RLVR to align LLM reasoning with structured evolutionary exploration using a composite reward that balances quality and diversity. Extensive experiments on multiple classification and regression datasets demonstrate that MORE-FE consistently outperforms state-of-the-art AFE methods, highlighting the effectiveness of balancing quality and diversity in feature engineering.
A three-paradigm taxonomy (feature-based, auxiliary-based, and policy-based) based on the functional role of LLMs within the RL pipeline is proposed, which provides superior scalability and stability, though often at the expense of representational depth.
Ghusoon Hadi al-Aldaffaie, Alireza Taheri, Amirfarhad Farhadi et al.· Discover Artificial Intellig...· 0 citations
RL-LLMfuzzer, a reinforcement learning and LLM-based differential fuzzing framework that has successfully unearthed 28 officially confirmed vulnerabilities in GCC and LLVM/Clang, establishing a highly efficient and scalable paradigm for LLM-driven compiler validation.
Donghui Li, Yingying Li, Bo Zhao et al.· Journal of King Saud Univers...· 0 citations
Reinforcement learning with verifiable rewards (RLVR) is rapidly advancing the reasoning capabilities of language models, yet the optimization layer that converts reward feedback into weight-space updates remains poorly understood. Building on our prior analysis (Zhu et al., 2025), we study this missing layer through the singular structure of model weights and identify spectral inheritance: RLVR can reuse the base model's weight spectra while acquiring new behavior through changes in the associated input and output singular frames. We operationalize spectral inheritance as Isospectral Optimization (ISO), an RLVR-native, fixed-spectrum optimization framework with complementary offline and online instantiations. Offline, ISO-Merger combines the frame changes of shared-base specialists into a single fixed-spectrum model, requiring no post-merge data, rollouts, gradient updates, or on-policy distillation (OPD). It recovers complementary specialist capabilities and achieves the strongest aggregate performance among the compared data-free merging methods. Online, ISO-Optimizer applies a chosen base optimizer, including AdamW and Muon, to the frame variables while keeping the base spectra fixed. Across reasoning and coding tasks ranging from 1.5B to 8B parameters, ISO-Optimizer improves accuracy in the reported runs and reaches matched scores with substantially fewer training steps. On Qwen3-8B-Base, AdamW reaches an aggregate accuracy of 0.495 after 270 training steps. ISO-AdamW reaches the same accuracy after only 100 training steps and improves further to 0.509 after 210 training steps. Together, ISO offers a concrete answer to RLVR's missing optimization layer: rather than inheriting pre-training optimization wholesale, design post-training around the structure of reward-driven adaptation: inherit the spectrum, optimize the frames.
CoFE (Collaborative Feature Engineering) is proposed, a two-phase framework that tightly couples search-based exploration with LLM-driven reasoning and consistently outperforms state-of-the-art data-driven and LLM-based AutoFE methods on the majority of datasets, while offering favorable computational efficiency.
Weihao Jiang, Ziang Nan, Zhihui Shi et al.· Proceedings of the 32nd ACM...· 0 citations
Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task. Among filter, wrapper, and embedded-based approaches, the reinforcement learning (RL)-based subspace exploration strategy provides a novel objective optimization-directed perspective and promising performance. Nevertheless, even with improved performance, current reinforcement learning approaches face challenges similar to conventional methods when dealing with complex datasets. These challenges stem from the inefficient paradigm of using one agent per feature and the inherent complexities present in the datasets. This observation motivates us to investigate and address the above issue and propose a novel approach, namely HRLFS. Our methodology initially employs a Large Language Model (LLM)-based hybrid state extractor to capture each feature's mathematical and semantic characteristics. Based on this information, features are clustered, facilitating the construction of hierarchical agents for each cluster and sub-cluster. Extensive experiments demonstrate the efficiency, scalability, and robustness of our approach. Compared to contemporary or the one-feature-one-agent RL-based approaches, HRLFS improves the downstream ML performance with iterative feature subspace exploration while accelerating total run time by reducing the number of agents involved. 1
Weiliang Zhang, Xiaohan Huang, Ziyue Qiao et al.· ACM Transactions on Knowledg...· 0 citations
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