It is found that, once prefill is measured without prefix caching, the recurrent architectures pay no prefill penalty: their linear-time mixing undercuts quadratic attention on long prompts, so their efficient decode adds to the prefill saving and lowers total request energy from the first output token at large batch.
Bo-Le Ma, Ayesha Afzal, Jan Eitzinger et al.· arXiv.org· 3 citations· ⚡1
Generative recommendation (GR) has emerged as a promising paradigm that predicts target items by autoregressively generating their semantic identifiers (SID). Most GR methods follow a quantization-representation-generation pipeline. While most research focuses on quantization and generation, the representation that bri...
Ziwei Liu, Yejing Wang, Shengyu Zhou et al.· 0 citations
DUET budget-aware control strategies are valuable not only for accelerating training, but also for improving the quality of the learning signal, contrary to the usual pattern in which efficient methods trade off quality as compute decreases.
Reinforcement learning (RL) with verifiable rewards has proven effective at post-training LLMs for coding, yet deploying separate task-specific specialists incurs costs that scale with the number of tasks, motivating a unified multi-task RL (MTRL) approach. However, existing MTRL methods treat all coding tasks uniforml...
Yujia Chen, Yang Ye, Xiao Chu et al.· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
Reasoning distillation aims to transfer multi-step reasoning capabilities from large language models to smaller, efficient ones. While recent methods have shown promising gains, they typically rely on static teacher-student hierarchies and frame distillation as trajectory imitation. This is misaligned with the structur...
Recent advances in large language models (LLMs) have led to the development of AI-powered tutoring systems that provide interactive support via dialogue. To enable these tutoring systems to provide personalized support, it is essential to assess student performance at each turn, motivating knowledge tracing (KT) in dia...
Shuyan Huang, Alexander Scarlatos, Jaewook Lee et al.· 0 citations
Low-Rank Adaptation (LoRA) is proposed as a structural regularizer for critic learning from scratch that reduces critic loss during training and improves overall policy performance, achieving the best or competitive results on most tasks.
Yuan Zhuang, Yuexin Bian, Si-Hong He et al.· arXiv.org· 0 citations
Retrieval-Augmented Generation (RAG) has become the standard paradigm for grounding Large Language Model outputs in external knowledge. Lumer et al. presented the first systematic evaluation comparing vector-based agentic RAG against hierarchical node-based reasoning systems for financial document QA across 1,200 SEC f...
AffordSim is introduced, a scalable data generator and benchmark that integrates open-vocabulary 3D affordance prediction into simulation-based trajectory generation and achieves 93% of the trajectory collection success rate of manual contact annotations on affordance-critical tasks and 89% on hard composite tasks.
Ming-Yang Li, Hao Xu, Hao-Wen Sun et al.· arXiv.org· 0 citations
The growing demand for deploying Small Language Models (SLMs) on edge devices, including laptops, smartphones, automotive and robotic computing platforms, and high-performance embedded systems, has exposed fundamental inefficiencies in existing accelerators. While GPUs handle prefill workloads efficiently, the autoregr...
Jinane Bazzi, Mariam Rakka, Fadi Kurdahi et al.· 0 citations
Generating realistic visual defects is crucial for alleviating the scarcity of abnormal samples in anomaly detection, yet existing methods often rely on category-specific few-shot adaptation and struggle to generalize across objects, defect types, and domains. This limitation stems in part from the lack of paired defec...
Yuanting Fan, Jun Liu, Bin-Bin Gao et al.· 0 citations
Neural routing solvers rarely stop at their first solution: constructed tours are usually refined by search, such as sampling or local search. Neural improvement methods learn this search as a policy that repeatedly applies local moves, but existing policies degrade sharply beyond their training size. We argue that siz...
