Cover Image

SLOT: Sample-specific Language Model Optimization at Test-time

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hu, Yang, Zhang, Xingyu, Fang, Xueji, Chen, Zhiyang, Wang, Xiao, Zhang, Huatian, Qi, Guojun
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SLOT: Structuring the Output of Large Language Models
by: Wang, Darren Yow-Bang, et al.
Published: (2025)

It's Not That Simple. An Analysis of Simple Test-Time Scaling
by: Wu, Guojun
Published: (2025)

Test-Time Learning for Large Language Models
by: Hu, Jinwu, et al.
Published: (2025)

FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees
by: Nie, Fan, et al.
Published: (2024)

Optimizing Temperature for Language Models with Multi-Sample Inference
by: Du, Weihua, et al.
Published: (2025)

DIDS: Domain Impact-aware Data Sampling for Large Language Model Training
by: Shi, Weijie, et al.
Published: (2025)

Accelerating Diffusion Large Language Models with SlowFast Sampling: The Three Golden Principles
by: Wei, Qingyan, et al.
Published: (2025)

Do Retrieval-Augmented Language Models Adapt to Varying User Needs?
by: Wu, Peilin, et al.
Published: (2025)

Generalizing Test-time Compute-optimal Scaling as an Optimizable Graph
by: Wang, Fali, et al.
Published: (2025)

Bridging the Knowledge Void: Inference-time Acquisition of Unfamiliar Programming Languages for Coding Tasks
by: Shen, Chen, et al.
Published: (2026)

Flaming-hot Initiation with Regular Execution Sampling for Large Language Models
by: Chen, Weizhe, et al.
Published: (2024)

Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization
by: Dey, Vishal, et al.
Published: (2025)

RuAG: Learned-rule-augmented Generation for Large Language Models
by: Zhang, Yudi, et al.
Published: (2024)

Large Language Models as Optimizers
by: Yang, Chengrun, et al.
Published: (2023)

Sample-aware Adaptive Structured Pruning for Large Language Models
by: Kong, Jun, et al.
Published: (2025)

Replay Failures as Successes: Sample-Efficient Reinforcement Learning for Instruction Following
by: Zhang, Kongcheng, et al.
Published: (2025)

AlignDistil: Token-Level Language Model Alignment as Adaptive Policy Distillation
by: Zhang, Songming, et al.
Published: (2025)

KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models
by: Zhang, Songming, et al.
Published: (2026)

Prompt Optimization Via Diffusion Language Models
by: Wang, Shiyu, et al.
Published: (2026)

Aioli: A Unified Optimization Framework for Language Model Data Mixing
by: Chen, Mayee F., et al.
Published: (2024)

TSO: Self-Training with Scaled Preference Optimization
by: Chen, Kaihui, et al.
Published: (2024)

A Neuro-inspired Interpretation of Unlearning in Large Language Models through Sample-level Unlearning Difficulty
by: Feng, Xiaohua, et al.
Published: (2025)

Language Model Prompt Selection via Simulation Optimization
by: Zhang, Haoting, et al.
Published: (2024)

Iterative Deepening Sampling as Efficient Test-Time Scaling
by: Chen, Weizhe, et al.
Published: (2025)

CLIPO: Contrastive Learning in Policy Optimization Generalizes RLVR
by: Cui, Sijia, et al.
Published: (2026)

DynaSpec: Context-aware Dynamic Speculative Sampling for Large-Vocabulary Language Models
by: Zhang, Jinbin, et al.
Published: (2025)

Causal Evaluation of Language Models
by: Chen, Sirui, et al.
Published: (2024)

PanGu-$π$ Pro:Rethinking Optimization and Architecture for Tiny Language Models
by: Tang, Yehui, et al.
Published: (2024)

Manifold-based Sampling for In-Context Hallucination Detection in Large Language Models
by: Vamshi, Bodla Krishna, et al.
Published: (2026)

TARo: Token-level Adaptive Routing for LLM Test-time Alignment
by: Rai, Arushi, et al.
Published: (2026)

Budget-aware Test-time Scaling via Discriminative Verification
by: Montgomery, Kyle, et al.
Published: (2025)

Towards the Law of Capacity Gap in Distilling Language Models
by: Zhang, Chen, et al.
Published: (2023)

Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models
by: Zhang, Wenxuan, et al.
Published: (2024)

FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling
by: Zhao, Weilin, et al.
Published: (2025)

MULTISCRIPT: Multimodal Script Learning for Supporting Open Domain Everyday Tasks
by: Qi, Jingyuan, et al.
Published: (2023)

AgentTTS: Large Language Model Agent for Test-time Compute-optimal Scaling Strategy in Complex Tasks
by: Wang, Fali, et al.
Published: (2025)

Towards Improved Preference Optimization Pipeline: from Data Generation to Budget-Controlled Regularization
by: Chen, Zhuotong, et al.
Published: (2024)

HAPO: Training Language Models to Reason Concisely via History-Aware Policy Optimization
by: Huang, Chengyu, et al.
Published: (2025)

Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL
by: Yao, Jiarui, et al.
Published: (2025)

Exploring Gradient-Guided Masked Language Model to Detect Textual Adversarial Attacks
by: Zhang, Xiaomei, et al.
Published: (2025)