:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Fan, Jingxuan, Li, Yueying, Qi, Zhenting, Zhang, Dinghuai, Brantley, Kianté, Kakade, Sham M., Zhang, Hanlin
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2603.02225
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems
by: Qi, Zhenting, et al.
Published: (2024)

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities
by: Zhang, Hanlin, et al.
Published: (2026)

EvoLM: In Search of Lost Language Model Training Dynamics
by: Qi, Zhenting, et al.
Published: (2025)

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning
by: Jin, Jikai, et al.
Published: (2025)

Connections between Schedule-Free Optimizers, AdEMAMix, and Accelerated SGD Variants
by: Morwani, Depen, et al.
Published: (2025)

RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
by: Oertell, Owen, et al.
Published: (2024)

Q-Probe: A Lightweight Approach to Reward Maximization for Language Models
by: Li, Kenneth, et al.
Published: (2024)

A Surprising Failure? Multimodal LLMs and the NLVR Challenge
by: Wu, Anne, et al.
Published: (2024)

CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training
by: Brandfonbrener, David, et al.
Published: (2024)

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models
by: Song, Yuda, et al.
Published: (2024)

Expressive Value Learning for Scalable Offline Reinforcement Learning
by: Espinosa-Dice, Nicolas, et al.
Published: (2025)

LLMs Are In-Context Bandit Reinforcement Learners
by: Monea, Giovanni, et al.
Published: (2024)

How Does Critical Batch Size Scale in Pre-training?
by: Zhang, Hanlin, et al.
Published: (2024)

Learning Hidden Markov Models Using Conditional Samples
by: Kakade, Sham M., et al.
Published: (2023)

Diffusing States and Matching Scores: A New Framework for Imitation Learning
by: Wu, Runzhe, et al.
Published: (2024)

Scaling Offline RL via Efficient and Expressive Shortcut Models
by: Espinosa-Dice, Nicolas, et al.
Published: (2025)

A Study on the Calibration of In-context Learning
by: Zhang, Hanlin, et al.
Published: (2023)

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

Adversarial Imitation Learning via Boosting
by: Chang, Jonathan D., et al.
Published: (2024)

Eliminating Position Bias of Language Models: A Mechanistic Approach
by: Wang, Ziqi, et al.
Published: (2024)

LLMs Can Learn to Reason Via Off-Policy RL
by: Ritter, Daniel, et al.
Published: (2026)

Random Scaling of Emergent Capabilities
by: Zhao, Rosie, et al.
Published: (2025)

Loss-to-Loss Prediction: Scaling Laws for All Datasets
by: Brandfonbrener, David, et al.
Published: (2024)

GQ-VAE: A gated quantized VAE for learning variable length tokens
by: Datta, Theo, et al.
Published: (2025)

Repeat After Me: Transformers are Better than State Space Models at Copying
by: Jelassi, Samy, et al.
Published: (2024)

$p1$: Better Prompt Optimization with Fewer Prompts
by: Gao, Zhaolin, et al.
Published: (2026)

Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond
by: Oncescu, Costin-Andrei, et al.
Published: (2024)

Scaling Laws for Imitation Learning in Single-Agent Games
by: Tuyls, Jens, et al.
Published: (2023)

Matching the Statistical Query Lower Bound for $k$-Sparse Parity Problems with Sign Stochastic Gradient Descent
by: Kou, Yiwen, et al.
Published: (2024)

The Potential of Second-Order Optimization for LLMs: A Study with Full Gauss-Newton
by: Abreu, Natalie, et al.
Published: (2025)

Accelerating RL for LLM Reasoning with Optimal Advantage Regression
by: Brantley, Kianté, et al.
Published: (2025)

Deconstructing What Makes a Good Optimizer for Language Models
by: Zhao, Rosie, et al.
Published: (2024)

Scaling Laws in Linear Regression: Compute, Parameters, and Data
by: Lin, Licong, et al.
Published: (2024)

Selective Underfitting in Diffusion Models
by: Song, Kiwhan, et al.
Published: (2025)

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
by: Kim, Jaeyeon, et al.
Published: (2025)

Universal Length Generalization with Turing Programs
by: Hou, Kaiying, et al.
Published: (2024)

REBEL: Reinforcement Learning via Regressing Relative Rewards
by: Gao, Zhaolin, et al.
Published: (2024)

Nabla-R2D3: Effective and Efficient 3D Diffusion Alignment with 2D Rewards
by: Liu, Qingming, et al.
Published: (2025)

Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training
by: Kim, Jaeyeon, et al.
Published: (2026)

In Good GRACEs: Principled Teacher Selection for Knowledge Distillation
by: Panigrahi, Abhishek, et al.
Published: (2025)