Saved in:
| Main Authors: | Chen, Fan, Huang, Audrey, Golowich, Noah, Malladi, Sadhika, Block, Adam, Ash, Jordan T., Krishnamurthy, Akshay, Foster, Dylan J. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.15020 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inference
by: Golowich, Noah, et al.
Published: (2026)
by: Golowich, Noah, et al.
Published: (2026)
Self-Improvement in Language Models: The Sharpening Mechanism
by: Huang, Audrey, et al.
Published: (2024)
by: Huang, Audrey, et al.
Published: (2024)
Representation-Based Exploration for Language Models: From Test-Time to Post-Training
by: Tuyls, Jens, et al.
Published: (2025)
by: Tuyls, Jens, et al.
Published: (2025)
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
by: Foster, Dylan J., et al.
Published: (2024)
by: Foster, Dylan J., et al.
Published: (2024)
Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment
by: Huang, Audrey, et al.
Published: (2025)
by: Huang, Audrey, et al.
Published: (2025)
Near-Optimal Learning and Planning in Separated Latent MDPs
by: Chen, Fan, et al.
Published: (2024)
by: Chen, Fan, et al.
Published: (2024)
Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration
by: Foster, Dylan J., et al.
Published: (2025)
by: Foster, Dylan J., et al.
Published: (2025)
Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification
by: Rohatgi, Dhruv, et al.
Published: (2025)
by: Rohatgi, Dhruv, et al.
Published: (2025)
In Good GRACEs: Principled Teacher Selection for Knowledge Distillation
by: Panigrahi, Abhishek, et al.
Published: (2025)
by: Panigrahi, Abhishek, et al.
Published: (2025)
Characterizing the Training-Conditional Coverage of Full Conformal Inference in High Dimensions
by: Gibbs, Isaac, et al.
Published: (2025)
by: Gibbs, Isaac, et al.
Published: (2025)
Metadata Conditioning Accelerates Language Model Pre-training
by: Gao, Tianyu, et al.
Published: (2025)
by: Gao, Tianyu, et al.
Published: (2025)
Post-Hoc Uncertainty Quantification in Pre-Trained Neural Networks via Activation-Level Gaussian Processes
by: Bergna, Richard, et al.
Published: (2025)
by: Bergna, Richard, et al.
Published: (2025)
Trainable Transformer in Transformer
by: Panigrahi, Abhishek, et al.
Published: (2023)
by: Panigrahi, Abhishek, et al.
Published: (2023)
Rate of convergence of the smoothed empirical Wasserstein distance
by: Block, Adam, et al.
Published: (2022)
by: Block, Adam, et al.
Published: (2022)
Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability
by: Chen, Fan, et al.
Published: (2024)
by: Chen, Fan, et al.
Published: (2024)
A Quantitative Characterization of Forgetting in Post-Training
by: Balasubramanian, Krishnakumar, et al.
Published: (2026)
by: Balasubramanian, Krishnakumar, et al.
Published: (2026)
Online Estimation via Offline Estimation: An Information-Theoretic Framework
by: Foster, Dylan J., et al.
Published: (2024)
by: Foster, Dylan J., et al.
Published: (2024)
Landauer Principle and Thermodynamics of Computation
by: Chattopadhyay, Pritam, et al.
Published: (2025)
by: Chattopadhyay, Pritam, et al.
Published: (2025)
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization
by: Huang, Audrey, et al.
Published: (2024)
by: Huang, Audrey, et al.
Published: (2024)
Laws of thermodynamics for exponential families
by: Balsubramani, Akshay
Published: (2025)
by: Balsubramani, Akshay
Published: (2025)
LESS: Selecting Influential Data for Targeted Instruction Tuning
by: Xia, Mengzhou, et al.
Published: (2024)
by: Xia, Mengzhou, et al.
Published: (2024)
Prediction Aided by Surrogate Training
by: Xia, Eric, et al.
Published: (2024)
by: Xia, Eric, et al.
Published: (2024)
Post-Hoc Large-Sample Statistical Inference
by: Chugg, Ben, et al.
Published: (2026)
by: Chugg, Ben, et al.
Published: (2026)
On Reconstructing Training Data From Bayesian Posteriors and Trained Models
by: Wynne, George
Published: (2025)
by: Wynne, George
Published: (2025)
Information theoretic limits of robust sub-Gaussian mean estimation under star-shaped constraints
by: Prasadan, Akshay, et al.
Published: (2024)
by: Prasadan, Akshay, et al.
Published: (2024)
Characterizing the minimax rate of nonparametric regression under bounded star-shaped constraints
by: Prasadan, Akshay, et al.
Published: (2024)
by: Prasadan, Akshay, et al.
Published: (2024)
Some facts about the optimality of the LSE in the Gaussian sequence model with convex constraint
by: Prasadan, Akshay, et al.
Published: (2024)
by: Prasadan, Akshay, et al.
Published: (2024)
Can large language models explore in-context?
by: Krishnamurthy, Akshay, et al.
Published: (2024)
by: Krishnamurthy, Akshay, et al.
Published: (2024)
High-dimensional (Group) Adversarial Training in Linear Regression
by: Xie, Yiling, et al.
Published: (2024)
by: Xie, Yiling, et al.
Published: (2024)
A Simplified and Numerically Stable Approach to the BG/NBD Churn Prediction model
by: Zammit, Dylan, et al.
Published: (2025)
by: Zammit, Dylan, et al.
Published: (2025)
Growth-Optimal E-Variables and an extension to the multivariate Csiszár-Sanov-Chernoff Theorem
by: Grünwald, Peter, et al.
Published: (2024)
by: Grünwald, Peter, et al.
Published: (2024)
Correcting the Coverage Bias of Quantile Regression
by: Gibbs, Isaac, et al.
Published: (2025)
by: Gibbs, Isaac, et al.
Published: (2025)
Dimension-free Bounds for Covariance Estimation with Tensor-Train Structure
by: Patarusau, Artsiom, et al.
Published: (2025)
by: Patarusau, Artsiom, et al.
Published: (2025)
Hallucinations are inevitable but can be made statistically negligible
by: Suzuki, Atsushi, et al.
Published: (2025)
by: Suzuki, Atsushi, et al.
Published: (2025)
Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization
by: Razin, Noam, et al.
Published: (2024)
by: Razin, Noam, et al.
Published: (2024)
A Few Observations on Sample-Conditional Coverage in Conformal Prediction
by: Duchi, John C.
Published: (2025)
by: Duchi, John C.
Published: (2025)
Source-Optimal Training is Transfer-Suboptimal
by: Hedges, C. Evans
Published: (2025)
by: Hedges, C. Evans
Published: (2025)
Global Convergence in Training Large-Scale Transformers
by: Gao, Cheng, et al.
Published: (2024)
by: Gao, Cheng, et al.
Published: (2024)
Rectifying Conformity Scores for Better Conditional Coverage
by: Plassier, Vincent, et al.
Published: (2025)
by: Plassier, Vincent, et al.
Published: (2025)
MUSE: Machine Unlearning Six-Way Evaluation for Language Models
by: Shi, Weijia, et al.
Published: (2024)
by: Shi, Weijia, et al.
Published: (2024)
Similar Items
-
Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inference
by: Golowich, Noah, et al.
Published: (2026) -
Self-Improvement in Language Models: The Sharpening Mechanism
by: Huang, Audrey, et al.
Published: (2024) -
Representation-Based Exploration for Language Models: From Test-Time to Post-Training
by: Tuyls, Jens, et al.
Published: (2025) -
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
by: Foster, Dylan J., et al.
Published: (2024) -
Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment
by: Huang, Audrey, et al.
Published: (2025)