:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Ji, Ke, Xu, Jiahao, Liang, Tian, Liu, Qiuzhi, He, Zhiwei, Chen, Xingyu, Liu, Xiaoyuan, Wang, Zhijie, Chen, Junying, Wang, Benyou, Tu, Zhaopeng, Mi, Haitao, Yu, Dong
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	https://arxiv.org/abs/2503.02875
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training
by: Wang, Mengru, et al.
Published: (2025)

Dancing with Critiques: Enhancing LLM Reasoning with Stepwise Natural Language Self-Critique
by: Li, Yansi, et al.
Published: (2025)

Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs
by: Wang, Yue, et al.
Published: (2025)

MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender
by: Wang, Bohao, et al.
Published: (2025)

Trust, But Verify: A Self-Verification Approach to Reinforcement Learning with Verifiable Rewards
by: Liu, Xiaoyuan, et al.
Published: (2025)

Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
by: Chen, Xingyu, et al.
Published: (2024)

DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning
by: He, Zhiwei, et al.
Published: (2025)

Insight Over Sight: Exploring the Vision-Knowledge Conflicts in Multimodal LLMs
by: Liu, Xiaoyuan, et al.
Published: (2024)

DeepTheorem: Advancing LLM Reasoning for Theorem Proving Through Natural Language and Reinforcement Learning
by: Zhang, Ziyin, et al.
Published: (2025)

Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability
by: Lin, Zicheng, et al.
Published: (2024)

PrefixQuant: Eliminating Outliers by Prefixed Tokens for Large Language Models Quantization
by: Chen, Mengzhao, et al.
Published: (2024)

RaSA: Rank-Sharing Low-Rank Adaptation
by: He, Zhiwei, et al.
Published: (2025)

Draft Model Knows When to Stop: Self-Verification Speculative Decoding for Long-Form Generation
by: Zhang, Ziyin, et al.
Published: (2024)

Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models
by: Li, Pengyi, et al.
Published: (2025)

Not All Tokens Are What You Need In Thinking
by: Yuan, Hang, et al.
Published: (2025)

Rho-1: Not All Tokens Are What You Need
by: Lin, Zhenghao, et al.
Published: (2024)

Zoom and Shift are All You Need
by: Qin, Jiahao
Published: (2024)

Emu3: Next-Token Prediction is All You Need
by: Wang, Xinlong, et al.
Published: (2024)

PrefixMemory-Tuning: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention
by: Wang, Haonan, et al.
Published: (2025)

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models
by: Ding, Shiwei, et al.
Published: (2025)

Search is All You Need for Few-shot Anomaly Detection
by: Wang, Qishan, et al.
Published: (2025)

Chain-of-Jailbreak Attack for Image Generation Models via Editing Step by Step
by: Wang, Wenxuan, et al.
Published: (2024)

Unsupervised Sign Language Translation and Generation
by: Guo, Zhengsheng, et al.
Published: (2024)

Ideal Registration? Segmentation is All You Need
by: Chen, Xiang, et al.
Published: (2025)

Supervised Fine-Tuning Needs to Unlock the Potential of Token Priority
by: Shen, Zhanming, et al.
Published: (2026)

SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN
by: You, Kang, et al.
Published: (2024)

RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions
by: Liu, Wanlong, et al.
Published: (2024)

Few Tokens, Big Leverage: Preserving Safety Alignment by Constraining Safety Tokens during Fine-tuning
by: Wang, Guoli, et al.
Published: (2026)

An Extra RMSNorm is All You Need for Fine Tuning to 1.58 Bits
by: Steinmetz, Cody, et al.
Published: (2025)

How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors
by: Xu, Yifan, et al.
Published: (2026)

All You Need is One: Capsule Prompt Tuning with a Single Vector
by: Liu, Yiyang, et al.
Published: (2025)

Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization
by: Xu, Zhitong, et al.
Published: (2024)

FineVision: Open Data Is All You Need
by: Wiedmann, Luis, et al.
Published: (2025)

IP-Adapter Is All You Need: Towards Fine-Tuning-Free Diffusion-Based Talking Face Generation
by: Wu, Hao, et al.
Published: (2026)

Teaching LLMs to Refine with Tools
by: Yu, Dian, et al.
Published: (2024)

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling
by: Huang, Zeyu, et al.
Published: (2025)

Unsupervised Real-World Denoising: Sparsity is All You Need
by: Chihaoui, Hamadi, et al.
Published: (2025)

Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering
by: Lyu, Xingyu, et al.
Published: (2025)

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning
by: Liu, Wanlong, et al.
Published: (2025)

Not All Documents Are What You Need for Extracting Instruction Tuning Data
by: Zhang, Chi, et al.
Published: (2025)