:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhang, Guanhua, Dominguez-Olmedo, Ricardo, Hardt, Moritz
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2507.05195
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Training on the Test Task Confounds Evaluation and Emergence
by: Dominguez-Olmedo, Ricardo, et al.
Published: (2024)

Computational Arbitrage in AI Model Markets
by: Olmedo, Ricardo, et al.
Published: (2026)

Leaderboard Incentives: Model Rankings under Strategic Post-Training
by: Chen, Yatong, et al.
Published: (2026)

Lawma: The Power of Specialization for Legal Annotation
by: Dominguez-Olmedo, Ricardo, et al.
Published: (2024)

Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks
by: Zhang, Guanhua, et al.
Published: (2024)

Learning on the Job: Test-Time Curricula for Targeted Reinforcement Learning
by: Hübotter, Jonas, et al.
Published: (2025)

MATH-Beyond: A Benchmark for RL to Expand Beyond the Base Model
by: Mayilvahanan, Prasanna, et al.
Published: (2025)

Answer Matching Outperforms Multiple Choice for Language Model Evaluation
by: Chandak, Nikhil, et al.
Published: (2025)

Test-Time Training on Nearest Neighbors for Large Language Models
by: Hardt, Moritz, et al.
Published: (2023)

Think before you speak: Training Language Models With Pause Tokens
by: Goyal, Sachin, et al.
Published: (2023)

How Benchmark Prediction from Fewer Data Misses the Mark
by: Zhang, Guanhua, et al.
Published: (2025)

Test-Time Training on Graphs with Large Language Models (LLMs)
by: Zhang, Jiaxin, et al.
Published: (2024)

Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
by: Shi, Haizhou, et al.
Published: (2024)

Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition
by: Gao, Xinyi, et al.
Published: (2024)

Policy-Gradient Training of Language Models for Ranking
by: Gao, Ge, et al.
Published: (2023)

Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training
by: Ye, Chenlu, et al.
Published: (2025)

Representation-Based Exploration for Language Models: From Test-Time to Post-Training
by: Tuyls, Jens, et al.
Published: (2025)

Low-Rank Compression of Language Models via Differentiable Rank Selection
by: Sundrani, Sidhant, et al.
Published: (2025)

D-QRELO: Training- and Data-Free Delta Compression for Large Language Models via Quantization and Residual Low-Rank Approximation
by: Li, Junlin, et al.
Published: (2026)

LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices
by: Lee, Jung Hyun, et al.
Published: (2024)

ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity
by: Li, Jiaxi, et al.
Published: (2026)

Large Language Model Compression with Global Rank and Sparsity Optimization
by: Zhou, Changhai, et al.
Published: (2025)

Utilizing Autoregressive Networks for Full Lifecycle Data Generation of Rolling Bearings for RUL Prediction
by: Wang, Junliang, et al.
Published: (2024)

SCATR: Simple Calibrated Test-Time Ranking
by: Shyamal, Divya, et al.
Published: (2026)

Unifying Perplexing Behaviors in Modified BP Attributions through Alignment Perspective
by: Zheng, Guanhua, et al.
Published: (2025)

Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Task
by: Engelhardt, Raphael C., et al.
Published: (2024)

Multimodal Survival Analysis with Locally Deployable Large Language Models
by: Gögl, Moritz, et al.
Published: (2026)

Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping
by: Wang, Guanhua, et al.
Published: (2024)

Query-Conditioned Test-Time Self-Training for Large Language Models
by: Song, Chaehee, et al.
Published: (2026)

Test Time Training for Supervised Causal Learning
by: Deng, Zizhen, et al.
Published: (2026)

FutureSim: Replaying World Events to Evaluate Adaptive Agents
by: Goel, Shashwat, et al.
Published: (2026)

Harnessing Orthogonality to Train Low-Rank Neural Networks
by: Coquelin, Daniel, et al.
Published: (2024)

LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models
by: Yang, Yifan, et al.
Published: (2024)

SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models
by: Tang, Anke, et al.
Published: (2024)

Preparing Lessons for Progressive Training on Language Models
by: Pan, Yu, et al.
Published: (2024)

CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation
by: Liu, Ziyue, et al.
Published: (2025)

Emergent Low-Rank Training Dynamics in MLPs with Smooth Activations
by: Xu, Alec S., et al.
Published: (2026)

Lotus: Efficient LLM Training by Randomized Low-Rank Gradient Projection with Adaptive Subspace Switching
by: Miao, Tianhao, et al.
Published: (2026)

Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training
by: Zhang, Chengqian, et al.
Published: (2026)

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
by: Zhang, Longteng, et al.
Published: (2026)