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新论文 · v1arXiv:2611.13482

ExRL: Sample-Efficient Online Adaptation of Long-Chunk Policies via Execution Length Control

Xu Luo, Jiaying Yang, Hao Wu, Junlin Xie, Zijin Hong, Ji Zhang, Lianli Gao, Jingkuan Song

原文摘要

Predicting long action chunks, often spanning dozens of timesteps, has become a key ingredient in increasingly capable robot policies. In this paper, we find that adapting these long-chunk policies with existing reinforcement learning (RL) methods can require many online samples due to the high dimensionality of the action chunks being optimized, making such adaptation impractical in real-world scenarios where interaction data are costly to collect. Our key insight is to shift adaptation from optimizing actions to optimizing how long to execute each predicted action chunk. This keeps the adaptation space low-dimensional while its expressivity grows with the chunk size. We thus introduce Execution at the Right Length (ExRL), a simple approach which augments the base policy with a lightweight adapter that predicts the execution length of each chunk and is trained using off-policy RL algorithms. Across simulation benchmarks and real-world robotic tasks, ExRL enables sample-efficient adaptation of a diverse range of long-chunk policies with minimal task-specific hyperparameter tuning.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11989

MUTE: Multi-Level Alignment Uncoupling Against Talking-Head Exploitation for Voice Protection

Donghyun Kim, Jin Hong, Seungmin Kim, Dain Kim, Junseok Kwon, Daeseon Choi

原文摘要

Talking-head generation models, which synthesize realistic facial animations from audio, are increasingly vulnerable to misuse in multimodal deepfake scenarios. Protecting such systems remains challenging, as talking-head models fundamentally rely on precise audio-visual alignment, while effective audio protection methods remain largely underexplored. In this work, we propose MUTE, an audio protection framework that uncouples the underlying audio-visual alignment through a multi-level strategy. Specifically, MUTE combines (1) Alignment-level Disruption, which directly perturbs cross-modal attention to disrupt structural alignment, and (2) Representation-level Degradation, which perturbs temporal audio embeddings to degrade their representations. To improve robustness, perturbations are constrained in the STFT domain to high-energy regions, making them resistant to post-processing, denoising, and adaptive attacks. An optional speaker-level objective further mitigates potential bypass via TTS-based resynthesis. Extensive experiments demonstrate that MUTE consistently degrades lip synchronization across both white-box and black-box talking-head models while preserving perceptual audio quality. The proposed method remains effective under real-world transformations and can be combined with image-based approaches to provide stronger multimodal protection. The source code and project page are available at https://github.com/jindl465/MUTE.

cs.SD · cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.19491

Activation-Keyed Momentum: An Anisotropic Momentum Update via the Delta Rule

Euijin Hong, Guannan Qu

原文摘要

Most modern optimizers form their momentum as an exponential moving average (EMA) of past gradients, forgetting every direction at one fixed rate. However, the inputs a deep network sees during training can be highly anisotropic, with a few directions queried frequently while most are seen rarely. Preconditioning methods address this anisotropy by wrapping extra processing around this buffer and leave the momentum update itself unchanged. We propose Activation-Keyed Momentum (AK-Momentum), which builds direction-awareness into the momentum update rule. The gradient of a linear layer splits into an input activation that acts as a $\textit{key}$ and an output-side error that acts as a $\textit{value}$. Keying on that activation, AK-Momentum updates the momentum buffer by the canonical delta rule, so each direction is forgotten at a rate set by how often it appears. We prove that it is a valid momentum, that it applies the input-side curvature correction without matrix inversion, and that it clears stale directions faster than EMA under both a fixed and a drifting optimum. It is a drop-in replacement for the momentum buffer of any optimizer, its coefficient transfers across widths under $μ$P, and its extra compute stays between $22.2\%$ and $25.0\%$ of a gated-MLP block's linear cost with no persistent memory. In FineWeb-Edu pretraining, AdamW with AK-Momentum (AK-AdamW) reaches AdamW's validation loss in up to $46.64 \pm 4.52\%$ fewer steps at 67M and $22.14 \pm 0.81\%$ at 370M over three seeds, and the gain persists at 1B on a Chinchilla-optimal budget. A Muon baseline tuned under the same protocol sits above AK-AdamW at both language-model scales, and the gain holds for SGD, ResNet-18, and ViT-Tiny on CIFAR-10. Training-time diagnostics confirm the predicted mechanism, better gradient tracking and healthier input directions. Code is available at https://github.com/euijinh/AK-Momentum.

cs.LG · cs.CL · math.OC · stat.ML

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2002ACL Anthology · ACL 2026

Do Morals Guide How LLMs Think? The Role of Ethical Perspectives in General Problem Solving

Iseo Kim, Eunjin Hong, Juae Kim Hankuk University of Foreign Studies

原文摘要

This study investigates how different moral conditions influence the general problem-solving capabilities of Large Language Models (LLMs). We aim to explore whether the role of morality as a cognitive function operating in human decision-making can be extended to LLMs. Specifically, we define distinct moral stages based on Kohlberg’s theory of moral development and design prompts to elicit model responses aligned with each corresponding condition. The validity of this alignment is verified using the Defining Issues Test, a human evaluation tool. Subsequently, models reflecting the characteristics of each condition are evaluated using the MMLU benchmark, which requires general problem-solving abilities across various domains. Experimental results show that different moral perspectives lead to changes in the model’s decision-making during general reasoning, reflected in both responses and internal representations. Notably, conditions grounded in more advanced moral stages tend to elicit more thoughtful and reflective problem-solving behavior, which is often associated with enhanced performance. Our study attempts to broaden the concept of LLM morality, which has traditionally only been considered in ethical judgment scenarios. Furthermore, it emphasizes that morality is not merely a means of ensuring safety but a crucial factor that shapes a model’s behavior and thought processes.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表