Cover Image

On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Shumin, Xie, Yuexiang, Zhang, Wenhao, Sun, Yuchang, Chen, Yanxi, Li, Yaliang, Zhang, Yanyong
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting
by: Zhang, Wenhao, et al.
Published: (2025)

IntentRL: Training Proactive User-intent Agents for Open-ended Deep Research via Reinforcement Learning
by: Luo, Haohao, et al.
Published: (2026)

EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models
by: Pan, Xuchen, et al.
Published: (2024)

R$^3$L: Reflect-then-Retry Reinforcement Learning with Language-Guided Exploration, Pivotal Credit, and Positive Amplification
by: Shi, Weijie, et al.
Published: (2026)

Exploring Selective Layer Fine-Tuning in Federated Learning
by: Sun, Yuchang, et al.
Published: (2024)

Group-Relative REINFORCE Is Secretly an Off-Policy Algorithm: Demystifying Some Myths About GRPO and Its Friends
by: Yao, Chaorui, et al.
Published: (2025)

Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of Large Language Models
by: Pan, Xuchen, et al.
Published: (2025)

Provable Scaling Laws for the Test-Time Compute of Large Language Models
by: Chen, Yanxi, et al.
Published: (2024)

Designing Algorithms Empowered by Language Models: An Analytical Framework, Case Studies, and Insights
by: Chen, Yanxi, et al.
Published: (2024)

Tree-based Models for Vertical Federated Learning: A Survey
by: Qian, Bingchen, et al.
Published: (2025)

EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
by: Chen, Yanxi, et al.
Published: (2023)

Are Large Language Models In-Context Graph Learners?
by: Li, Jintang, et al.
Published: (2025)

Preserving Diversity in Supervised Fine-Tuning of Large Language Models
by: Li, Ziniu, et al.
Published: (2024)

Enhancing Latent Computation in Transformers with Latent Tokens
by: Sun, Yuchang, et al.
Published: (2025)

Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning
by: Zhai, Yuexiang, et al.
Published: (2024)

Boosting Large Language Models with Mask Fine-Tuning
by: Zhang, Mingyuan, et al.
Published: (2025)

Agent-Oriented Planning in Multi-Agent Systems
by: Li, Ao, et al.
Published: (2024)

Reinforcement Learning Fine-Tunes a Sparse Subnetwork in Large Language Models
by: Balashov, Andrii
Published: (2025)

TCOD: Exploring Temporal Curriculum in On-Policy Distillation for Multi-turn Autonomous Agents
by: Wang, Jiaqi, et al.
Published: (2026)

Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning
by: Ye, Kai, et al.
Published: (2025)

Improving Large Language Models with Concept-Aware Fine-Tuning
by: Chen, Michael K., et al.
Published: (2025)

Fine-Tuning is Subgraph Search: A New Lens on Learning Dynamics
by: Li, Yueyan, et al.
Published: (2025)

Linear Chain Transformation: Expanding Optimization Dynamics for Fine-Tuning Large Language Models
by: Wang, Yulong, et al.
Published: (2024)

Policy Gradient with Adaptive Entropy Annealing for Continual Fine-Tuning
by: Zhang, Yaqian, et al.
Published: (2026)

Linearization Explains Fine-Tuning in Large Language Models
by: Afzal, Zahra Rahimi, et al.
Published: (2026)

Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions
by: Zhang, Wenhao, et al.
Published: (2026)

On Predictability of Reinforcement Learning Dynamics for Large Language Models
by: Cai, Yuchen, et al.
Published: (2025)

A Bayesian Hybrid Parameter-Efficient Fine-Tuning Method for Large Language Models
by: Chai, Yidong, et al.
Published: (2025)

RPO:Reinforcement Fine-Tuning with Partial Reasoning Optimization
by: Yi, Hongzhu, et al.
Published: (2026)

QuantLRM: Quantization of Large Reasoning Models via Fine-Tuning Signals
by: Zhang, Nan, et al.
Published: (2026)

Double-I Watermark: Protecting Model Copyright for LLM Fine-tuning
by: Li, Shen, et al.
Published: (2024)

Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models
by: Fang, Zihan, et al.
Published: (2024)

Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data
by: Ling, Zhenqing, et al.
Published: (2025)

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

QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models
by: Zhou, Jiajun, et al.
Published: (2025)

FedKRSO: Communication and Memory Efficient Federated Fine-Tuning of Large Language Models
by: Yang, Guohao, et al.
Published: (2026)

LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
by: Zhang, Yuanhe, et al.
Published: (2025)

MaZO: Masked Zeroth-Order Optimization for Multi-Task Fine-Tuning of Large Language Models
by: Zhang, Zhen, et al.
Published: (2025)

Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning
by: Zhang, Minmin, et al.
Published: (2026)

Instruction Tuning for Large Language Models: A Survey
by: Zhang, Shengyu, et al.
Published: (2023)