:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Kim, Junhyuck, Ewer, Ethan, Moon, Taehong, Park, Jongho, Papailiopoulos, Dimitris
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2510.10964
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Lexico: Extreme KV Cache Compression via Sparse Coding over Universal Dictionaries
by: Kim, Junhyuck, et al.
Published: (2024)

Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
by: Park, Jongho, et al.
Published: (2024)

VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data
by: Zeng, Thomas, et al.
Published: (2025)

Efficient Generative Modeling with Residual Vector Quantization-Based Tokens
by: Kim, Jaehyeon, et al.
Published: (2024)

Beyond RLHF: A Unified Theoretical Framework of Alignment
by: Yun, Jihun, et al.
Published: (2025)

Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning
by: Shrivastava, Vaishnavi, et al.
Published: (2025)

Wait, Wait, Wait... Why Do Reasoning Models Loop?
by: Pipis, Charilaos, et al.
Published: (2025)

Endless Terminals: Scaling RL Environments for Terminal Agents
by: Gandhi, Kanishk, et al.
Published: (2026)

Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance
by: Park, Dongmin, et al.
Published: (2024)

Not All LLM Reasoners Are Created Equal
by: Hosseini, Arian, et al.
Published: (2024)

ENTP: Encoder-only Next Token Prediction
by: Ewer, Ethan, et al.
Published: (2024)

Looped Transformers are Better at Learning Learning Algorithms
by: Yang, Liu, et al.
Published: (2023)

From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data
by: Xiong, Zheyang, et al.
Published: (2024)

Effective Test-Time Scaling of Discrete Diffusion through Iterative Refinement
by: Lee, Sanghyun, et al.
Published: (2025)

Task Vectors in In-Context Learning: Emergence, Formation, and Benefit
by: Yang, Liu, et al.
Published: (2025)

Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges
by: Lee, Nayoung, et al.
Published: (2025)

Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding
by: Yang, Seongjun, et al.
Published: (2023)

Lookahead Unmasking Elicits Accurate Decoding in Diffusion Language Models
by: Lee, Sanghyun, et al.
Published: (2025)

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess
by: Hwang, Dongyoon, et al.
Published: (2025)

ReJump: A Tree-Jump Representation for Analyzing and Improving LLM Reasoning
by: Zeng, Yuchen, et al.
Published: (2025)

Pruning and Distilling Mixture-of-Experts into Dense Language Models
by: Kim, Junhyuck, et al.
Published: (2026)

How Well Can Transformers Emulate In-context Newton's Method?
by: Giannou, Angeliki, et al.
Published: (2024)

Memory-Efficient Personalization of Text-to-Image Diffusion Models via Selective Optimization Strategies
by: Choi, Seokeon, et al.
Published: (2025)

Not All Forgetting Is Equal: Architecture-Dependent Retention Dynamics in Fine-Tuned Image Classifiers
by: Daga, Miit, et al.
Published: (2026)

Not All Code Is Equal: A Data-Centric Study of Code Complexity and LLM Reasoning
by: Twist, Lukas, et al.
Published: (2026)

Not All Negative Samples Are Equal: LLMs Learn Better from Plausible Reasoning
by: Di, Zixiang, et al.
Published: (2026)

MSQ: Memory-Efficient Bit Sparsification Quantization
by: Han, Seokho, et al.
Published: (2025)

Selective Matching Losses -- Not All Scores Are Created Equal
by: Shamir, Gil I., et al.
Published: (2025)

MAGNET: Autonomous Expert Model Generation via Decentralized Autoresearch and BitNet Training
by: Kim, Yongwan, et al.
Published: (2026)

Process-Aware Procurement Lead Time Prediction for Shipyard Delay Mitigation
by: Lee, Yongjae, et al.
Published: (2026)

Adaptive Tracking of a Single-Rigid-Body Character in Various Environments
by: Kwon, Taesoo, et al.
Published: (2023)

Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs
by: Xu, Binxing, et al.
Published: (2026)

Not All Denoising Steps Are Equal: Model Scheduling for Faster Masked Diffusion Language Models
by: Sedykh, Ivan, et al.
Published: (2026)

Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models
by: Kim, Yeongmin, et al.
Published: (2026)

CHAI: Clustered Head Attention for Efficient LLM Inference
by: Agarwal, Saurabh, et al.
Published: (2024)

Taming Gradient Oversmoothing and Expansion in Graph Neural Networks
by: Park, MoonJeong, et al.
Published: (2024)

Parareal Neural Networks Emulating a Parallel-in-time Algorithm
by: Lee, Chang-Ock, et al.
Published: (2021)

All Nodes are created Not Equal: Node-Specific Layer Aggregation and Filtration for GNN
by: Wang, Shilong, et al.
Published: (2024)

Didactic to Constructive: Turning Expert Solutions into Learnable Reasoning
by: Mendes, Ethan, et al.
Published: (2026)

MIDUS: Memory-Infused Depth Up-Scaling
by: Kim, Taero, et al.
Published: (2025)