Copertina

CurveRL: Principled Distribution-Aware Context Reweighting for LLM Reasoning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Sun, Ke, Zhao, Yizhou, Xin, Jiayi, Long, Qi, Su, Weijie
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Mitigating Think-Answer Mismatch in LLM Reasoning Through Noise-Aware Advantage Reweighting
di: Shen, Si, et al.
Pubblicazione: (2025)

Robust Spectral Watermark for Synthetic Tabular Data
di: Zhao, Yizhou, et al.
Pubblicazione: (2025)

UCS: Estimating Unseen Coverage for Improved In-Context Learning
di: Xin, Jiayi, et al.
Pubblicazione: (2026)

Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know?
di: Li, Xiang, et al.
Pubblicazione: (2025)

Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
di: Zheng, Weihuang, et al.
Pubblicazione: (2024)

The Impact of Language Mixing on Bilingual LLM Reasoning
di: Li, Yihao, et al.
Pubblicazione: (2025)

A Principled Path to Fitted Distributional Evaluation
di: Hong, Sungee, et al.
Pubblicazione: (2025)

The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving
di: Zeng, Pai, et al.
Pubblicazione: (2024)

FlowRL: Matching Reward Distributions for LLM Reasoning
di: Zhu, Xuekai, et al.
Pubblicazione: (2025)

Bridging the Gap: Rademacher Complexity in Robust and Standard Generalization
di: Xiao, Jiancong, et al.
Pubblicazione: (2024)

DAJ: Data-Reweighted LLM Judge for Test-Time Scaling in Code Generation
di: Qin, Peijia, et al.
Pubblicazione: (2026)

Don't Waste Mistakes: Leveraging Negative RL-Groups via Confidence Reweighting
di: Feng, Yunzhen, et al.
Pubblicazione: (2025)

On Context-Content Uncertainty Principle
di: Li, Xin
Pubblicazione: (2025)

PickLLM: Context-Aware RL-Assisted Large Language Model Routing
di: Sikeridis, Dimitrios, et al.
Pubblicazione: (2024)

RaaS: Reasoning-Aware Attention Sparsity for Efficient LLM Reasoning
di: Hu, Junhao, et al.
Pubblicazione: (2025)

Online LLM watermark detection via e-processes
di: Su, Weijie, et al.
Pubblicazione: (2026)

Constrained Reweighting of Distributions: an Optimal Transport Approach
di: Chakraborty, Abhisek, et al.
Pubblicazione: (2023)

Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation
di: Ding, Yanna, et al.
Pubblicazione: (2024)

DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning
di: Cao, Qi, et al.
Pubblicazione: (2025)

Enhancing RL Safety with Counterfactual LLM Reasoning
di: Gross, Dennis, et al.
Pubblicazione: (2024)

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

Improving RL Exploration for LLM Reasoning through Retrospective Replay
di: Dou, Shihan, et al.
Pubblicazione: (2025)

Token-Efficient RL for LLM Reasoning
di: Lee, Alan, et al.
Pubblicazione: (2025)

Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?
di: Chen, Shuxiao, et al.
Pubblicazione: (2021)

Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching
di: Shi, Zhekun, et al.
Pubblicazione: (2025)

Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control
di: Li, Bolian, et al.
Pubblicazione: (2026)

PolarGrad: A Class of Matrix-Gradient Optimizers from a Unifying Preconditioning Perspective
di: Lau, Tim Tsz-Kit, et al.
Pubblicazione: (2025)

An Empirical Study on the Effectiveness of Incorporating Offline RL As Online RL Subroutines
di: Su, Jianhai, et al.
Pubblicazione: (2025)

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning
di: Yang, Puning, et al.
Pubblicazione: (2025)

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning
di: Ebrahimpour-Boroojeny, Ali, et al.
Pubblicazione: (2025)

DISA: Offline Importance Sampling for Distribution-Matching LLM-RL
di: Wang, Shaobo, et al.
Pubblicazione: (2026)

Learning to Extract Context for Context-Aware LLM Inference
di: Kim, Minseon, et al.
Pubblicazione: (2025)

Out-of-Distribution Adaptation in Offline RL: Counterfactual Reasoning via Causal Normalizing Flows
di: Cho, Minjae, et al.
Pubblicazione: (2024)

On the Limits of Sparse Autoencoders: A Theoretical Framework and Reweighted Remedy
di: Cui, Jingyi, et al.
Pubblicazione: (2025)

Beat the long tail: Distribution-Aware Speculative Decoding for RL Training
di: Shao, Zelei, et al.
Pubblicazione: (2025)

Contextual Bandits for Unbounded Context Distributions
di: Zhao, Puning, et al.
Pubblicazione: (2024)

Beyond High-Entropy Exploration: Correctness-Aware Low-Entropy Segment-Based Advantage Shaping for Reasoning LLMs
di: Chen, Xinzhu, et al.
Pubblicazione: (2025)

Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach
di: Xiao, Jiancong, et al.
Pubblicazione: (2025)

Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning
di: Liu, Zihe, et al.
Pubblicazione: (2025)

DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning
di: Zhang, Kaichuang, et al.
Pubblicazione: (2025)