Cover Image

Weak-to-Strong Generalization under Distribution Shifts

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jeon, Myeongho, Sobotka, Jan, Choi, Suhwan, Brbić, Maria
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

When Weak LLMs Speak with Confidence, Preference Alignment Gets Stronger
by: Afzali, Amirabbas, et al.
Published: (2026)

An Analysis of Model Robustness across Concurrent Distribution Shifts
by: Jeon, Myeongho, et al.
Published: (2025)

Feature-aligned N-BEATS with Sinkhorn divergence
by: Lee, Joonhun, et al.
Published: (2023)

Learning Stable Predictors from Weak Supervision under Distribution Shift
by: Shoeibi, Mehrdad, et al.
Published: (2026)

Graphs Generalization under Distribution Shifts
by: Tian, Qin, et al.
Published: (2024)

Quantifying the Gain in Weak-to-Strong Generalization
by: Charikar, Moses, et al.
Published: (2024)

No Prompt Left Behind: Exploiting Zero-Variance Prompts in LLM Reinforcement Learning via Entropy-Guided Advantage Shaping
by: Le, Thanh-Long V., et al.
Published: (2025)

Bias as a Virtue: Rethinking Generalization under Distribution Shifts
by: Chen, Ruixuan, et al.
Published: (2025)

An Information Theoretic Evaluation Metric For Strong Unlearning
by: Jeon, Dongjae, et al.
Published: (2024)

Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models
by: Pawelczyk, Martin, et al.
Published: (2024)

Weak-to-Strong Generalization Through the Data-Centric Lens
by: Shin, Changho, et al.
Published: (2024)

Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts
by: Navarro, Maria Conchita Agana, et al.
Published: (2026)

Meta-RL Induces Exploration in Language Agents
by: Jiang, Yulun, et al.
Published: (2025)

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions
by: Xue, Yihao, et al.
Published: (2025)

Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
by: Zhang, Zeyang, et al.
Published: (2024)

Mixture of Weak & Strong Experts on Graphs
by: Zeng, Hanqing, et al.
Published: (2023)

Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon Game
by: Karabag, Mustafa O., et al.
Published: (2025)

Addressing Label Shift in Distributed Learning via Entropy Regularization
by: Wu, Zhiyuan, et al.
Published: (2025)

Robust Uncertainty Estimation under Distribution Shift via Difference Reconstruction
by: Xu, Xinran, et al.
Published: (2026)

Causal-aware Graph Neural Architecture Search under Distribution Shifts
by: Li, Peiwen, et al.
Published: (2024)

Seen-to-Scene: Keep the Seen, Generate the Unseen for Video Outpainting
by: Jeon, Inseok, et al.
Published: (2026)

Bayesian WeakS-to-Strong from Text Classification to Generation
by: Cui, Ziyun, et al.
Published: (2024)

Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL
by: Yao, Wei, et al.
Published: (2025)

With Limited Data for Multimodal Alignment, Let the STRUCTURE Guide You
by: Gröger, Fabian, et al.
Published: (2025)

Adaptive Individual Uncertainty under Out-Of-Distribution Shift with Expert-Routed Conformal Prediction
by: Badkul, Amitesh, et al.
Published: (2025)

Transferable Model-agnostic Vision-Language Model Adaptation for Efficient Weak-to-Strong Generalization
by: Park, Jihwan, et al.
Published: (2025)

GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts
by: Zou, Deyu, et al.
Published: (2023)

Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts
by: Jiang, Haiyang, et al.
Published: (2024)

Few-shot Cross-country Generalization of Tabular Machine Learning and Foundation Models for Childhood Anemia Prediction under Distribution Shift
by: Brima, Yusuf, et al.
Published: (2026)

WST: Weak-to-Strong Knowledge Transfer via Reinforcement Learning
by: Ge, Haosen, et al.
Published: (2025)

When to Trust the Cheap Check: Weak and Strong Verification for Reasoning
by: Kiyani, Shayan, et al.
Published: (2026)

Revisiting the Platonic Representation Hypothesis: An Aristotelian View
by: Gröger, Fabian, et al.
Published: (2026)

Graph Fairness Learning under Distribution Shifts
by: Li, Yibo, et al.
Published: (2024)

PIMSM: Physics-Informed Multi-Scale Mamba for Stable Neural Representations under Distribution Shift
by: Bae, Sangyoon, et al.
Published: (2026)

Dynamics-Aligned Shared Hypernetworks for Contextual RL under Discontinuous Shifts
by: Benad, Jan, et al.
Published: (2026)

Deceptive Risk Minimization: Out-of-Distribution Generalization by Deceiving Distribution Shift Detectors
by: Majumdar, Anirudha
Published: (2025)

UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts
by: Wang, Tongze, et al.
Published: (2026)

Sufficient Invariant Learning for Distribution Shift
by: Kim, Taero, et al.
Published: (2022)

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One
by: Song, Yiwen, et al.
Published: (2025)

Sample-Efficient Adaptation of Drug-Response Models to Patient Tumors under Strong Biological Domain Shift
by: Cortes, Camille Jimenez, et al.
Published: (2026)