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新论文 · v1arXiv:2611.16183

Data-Efficient LLM Safety Alignment Guided by Interpretable Features

Hongyu Li, Zhan Yu, Zhe Zhao, Kimmo Yan, Xiong Kuang, Renfen Hu

原文摘要

Existing large language models typically rely on massive fine-tuning datasets for safety alignment, which results in higher costs for safety alignment and often leads to the problem of over-refusal. We propose Less is Safer (LIS), a compact yet highly effective dataset for safety alignment, constructed through interpretable feature-guided selection and adversarial synthesis. Specifically, we leverage Sparse Autoencoder (SAE) activations to efficiently select prompts that cover diverse and interpretable safety features. Then, we employ a multi-agent adversarial synthesis framework to generate harmless prompts that nevertheless elicit unnecessary refusals. LIS contains only 2.4K samples, yet outperforms much larger datasets, e.g., TRIDENT-Core (26K), TRIDENT-Edge (19K) and WildJailbreak (262K), in overall performance on four safety and five over-refusal benchmarks. Through ablation experiments, we demonstrate that neither safety data nor over-refusal data alone can achieve optimal results. We also used safety features from SAE, annotated by an LLM, for data filtering, but the results were inferior to those obtained using the pipeline proposed in this paper.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09091

TUGB: A Two-stage Uncertainty-aware Generative Auto-Bidding Framework

Luzhan Yuan, Chao Mi, Xiahao Shi, Yi Mao, Shengsheng Niu, Xin Zhu, Jie He, Zhangang Lin 等 9 位作者

原文摘要

Generative modeling, particularly Decision Transformers, shows promise in auto-bidding strategies. However, it struggles with limited exploration in static offline datasets, vulnerability to dynamic environmental shifts, and a reliance on heuristic return-to-go (RTG) specifications that may yield suboptimal performance on unseen traffic. To address these challenges, we propose the Two-stage Uncertainty-aware Generative Auto-Bidding (TUGB) framework. TUGB integrates generative sequence modeling with Reinforcement Learning (RL). It employs a two-stage pipeline where a pre-trained Generative Bidding Model is fine-tuned via an ensemble-based Uncertainty-aware Environment Model. This approach quantifies epistemic uncertainty to penalize high-risk trajectories and evolves the policy from RTG-conditioning to direct bidding optimization. Extensive experiments demonstrate that TUGB significantly outperforms SOTA baselines. Notably, full-scale deployment on JD Search Ad achieved a 2.32\% increase in cost and 3.57\% in target cost, validating its industrial efficacy, while its successful adaptation to subsequent mechanism upgrades and JD Food Delivery Ad further validates TUGB's superior generalization capability.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.28610

Learning Arbitrary Lindbladians from Time Evolution

Zhili Chen, Zhan Yu

原文摘要

We study the problem of learning arbitrary $n$-qubit Lindbladians, the generators of Markovian open-system dynamics, from their time evolution. We give the first known efficient algorithm that learns arbitrary Lindbladians without any structural assumptions. For an $n$-qubit Lindbladian of dynamical strength at most $Λ$, the algorithm estimates every coefficient to error $\varepsilon$ using $\widetilde O(Λ^2/\varepsilon^2)$ experiments and $\widetilde O(Λ/\varepsilon^2)$ total evolution time, with classical running time polynomial in $n$ and $Λ/\varepsilon$. Both experimental costs match known lower bounds up to polylogarithmic factors. The algorithm identifies candidate labels through product Pauli experiments and estimates their coefficients using random stabilizer preparations and Clifford measurements. It requires no ancillas or control during the unknown evolution. For constant $k$, our algorithm learns generic $k$-local Lindbladians of local dynamical strength at most $Λ_{\mathrm{loc}}$ using $\widetilde O(n^2Λ_{\mathrm{loc}}^2/\varepsilon^2)$ experiments and $\widetilde O(nΛ_{\mathrm{loc}}/\varepsilon^2)$ total evolution time, improving the upper bounds of prior works in terms of the dependence on $n$. We further improve the lower bounds of Arad, Chen, Guo, Rebentrost, and Yu (SODA 2027) using a family of $3$-local Lindbladians. They already hold when the support is known, and only one Hamiltonian coefficient is requested, even for adaptive protocols with arbitrary ancillas and quantum memory. These lower bounds establish that our algorithm is optimal up to polylogarithmic factors in both experiment count and total evolution time for every fixed $k \geq 3$.

quant-ph · cs.DS

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arXiv ↗PDF ↗