Regularization

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Regularization Techniques

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Kullback-Leibler Divergence

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Divergence

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Bregman Divergences

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Entropy-Regularized Equilibrium Selection

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Kullback-Leibler Regularization

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Quantitative Idea Divergence

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638 papers

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ERPPO: Entropy Regularization-based Proximal Policy Optimization
May 13, 2026Changha Lee, Gyusang ChoProximal Policy Optimization Multi-Agent Reinforcement Learning
Lost and Found in Translation: Variational Diagnostics for Neural Codebook Channels
May 13, 2026Yusuke HayashiVariational Autoencoder Codebooks
Offline Two-Player Zero-Sum Markov Games with KL Regularization
May 13, 2026Claire Chen, Yuheng Zhang, Xinyu Liu +3Nash Equilibrium Mirror Descent
The Growing Pains of Frontier Models: When Leaderboards Stop Separating and What to Measure Next
May 13, 2026Adil AminFrontier Models Leaderboard
Graph-Based Financial Fraud Detection with Calibrated Risk Scoring and Structural Regularization
May 12, 2026Yunfei Nie, Jiawei Wang, Ruobing Yan +3Fraud Detection Graph Representation Learning
Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data
May 12, 2026Paul Hoareau, Kuan Yi Wang, Brandon Bujak +6Medical Image Segmentation 3D Semantic Segmentation
Trust the Batch, On- or Off-Policy: Adaptive Policy Optimization for RL Post-Training
May 12, 2026Rasool Fakoor, Murdock Aubry, Nicholas Stranges +1Batch Size Hyperparameter Optimization
Autoregressive Learning in Joint KL: Sharp Oracle Bounds and Lower Bounds
May 12, 2026Yunbei Xu, Yuzhe Yuan, Ruohan ZhanKullback-Leibler Divergence Autoregressive Model
KAN-CL: Per-Knot Importance Regularization for Continual Learning with Kolmogorov-Arnold Networks
May 12, 2026Minjong CheonCatastrophic Forgetting Kolmogorov-Arnold Networks
Why Conclusions Diverge from the Same Observations: Formalizing World-Model Non-Identifiability via an Inference
May 12, 2026Toru TakahashiIdentifiability Disagreement
On What We Can Learn from Low-Resolution Data
May 12, 2026Theresa Dahl Frehr, Niels Henrik Pontoppidan, Hiba Nassar +1Low-Resolution Downstream Fine-Tuning
PairDropGS: Paired Dropout-Induced Consistency Regularization for Sparse-View Gaussian Splatting
May 12, 2026Hantang Li, Qiang Zhu, Xiandong Meng +33D Gaussian Splats Gaussian Splatting
Information theoretic underpinning of self-supervised learning by clustering
May 12, 2026Josef Kittler, Sara Atito, Muhammad AwaisSelf-Supervised Kullback-Leibler Divergence
Logit-Attention Divergence: Mitigating Position Bias in Multi-Image Retrieval via Attention-Guided Calibration
May 12, 2026Mingtao Xian, Yifeng Yang, Qinying Gu +2Multi-Modal Large Language Models Cross-Modal Attention
Understanding and Preventing Entropy Collapse in RLVR with On-Policy Entropy Flow Optimization
May 12, 2026Huimin Xu, Shuai Zhao, Xiaobao Wu +1Reinforcement Learning With Verifiable Reward Flow-Based Policy
fg-expo: Frontier-guided exploration-prioritized policy optimization via adaptive kl and gaussian curriculum
May 12, 2026Mingxiong Lin, Zhangquan Gong, Maowen Tang +6Group Relative Policy Optimization Reinforcement Learning With Verifiable Reward
The tractability landscape of diffusion alignment: regularization, rewards, and computational primitives
May 12, 2026Ankur Moitra, Andrej Risteski, Dhruv RohatgiRegularization Techniques Computable
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May 11, 2026Siqi Zhu, Xuyan Ye, Hongyu Lu +2On-Policy Distillation Teacher
Exact Unlearning from Proxies Induces Closeness Guarantees on Approximate Unlearning
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A Spectral Framework for Closed-Form Relative Density Estimation
May 11, 2026Francis BachDensity Ratio Estimation Bregman Divergences
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May 11, 2026Venugopalan IyengarQuantization Large Language Model Decoder
CausalGS: Learning Physical Causality of 3D Dynamic Scenes with Gaussian Representations
May 11, 2026Nengbo Lu, Minghua PanFuture Video Prediction 3D Motion
Identified-Set Geometry of Distributional Model Extraction under Top- $K$ Censored API Access
May 11, 2026Wenhua Nie, ZiCheng Zhu, Jianan Wu +3Kullback-Leibler Divergence Top-K
Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation
May 11, 2026Lucas Morisset, Alain Durmus, Adrien HardySynthetic Data Augmentation Regularization Techniques
TRACE: Distilling Where It Matters via Token-Routed Self On-Policy Alignment
May 11, 2026Jiaxuan Wang, Xuan Ouyang, Zhiyu Chen +4Unsupervised On-Policy Self-Distillation On-Policy Distillation
OUIDecay: Adaptive Layer-wise Weight Decay for CNNs Using Online Activation Patterns
May 11, 2026Alberto Fernández-Hernández, Jose I. Mestre, Cristian Pérez-Corral +3Convolutional Neural Networks Regularization Techniques
A Stability Benchmark of Generative Regularizers for Inverse Problems
May 11, 2026Alexander Denker, Johannes Hertrich, Sebastian NeumayerIll-Posed Inverse Problem Inverse Problem
Differentially Private Sampling from Distributions via Wasserstein Projection
May 11, 2026Shokichi Takakura, Seng Pei Liew, Satoshi HasegawaDifferential Privacy Wasserstein Distance
Optimizer-Induced Mode Connectivity: From AdamW to Muon
May 11, 2026Fangzhao Zhang, Sungyoon Kim, Erica Zhang +2Rectified Linear Unit Networks Regularization Techniques
expo: Exploration-prioritized policy optimization via adaptive kl regulation and gaussian curriculum sampling
May 11, 2026Mingxiong Lin, Zhangquan Gong, Maowen Tang +6Group Relative Policy Optimization Reinforcement Learning With Verifiable Reward
TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM
May 10, 2026Haoyang Zhou, Li Kong, Shijie Ren +4Diffusion Language Models Kullback-Leibler Divergence
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Reinforcing Multimodal Reasoning Against Visual Degradation
May 10, 2026Rui Liu, Dian Yu, Haolin Liu +6Multimodal Reasoning Benchmarks Multimodal Reasoning
How Much is Brain Data Worth for Machine Learning?
May 10, 2026Lane Lewis, Zhixin Wang, David Schwab +1Machine Learning Sample Size
Sub-JEPA: Subspace Gaussian Regularization for Stable End-to-End World Models
May 10, 2026Kai Zhao, Dongliang Nie, Yuchen Lin +4Joint-Embedding Predictive Architectures Latent World Models
Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability
May 9, 2026Qingyue Zhao, Kaixuan Ji, Heyang Zhao +1Kullback-Leibler Regularization Optimal Sample Complexity
Objective-Specific Privileged Bases via Full-Prefix Matryoshka Learning
May 9, 2026Arghamitra Talukder, Philippe Chlenski, Itsik Pe'erRepresentation Learning Matryoshka Representation Learning
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Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach
May 9, 2026Hira Nasir, Eiman Javed, Balawal Shabir +2Intrusion Detection Adversarial Transferability
Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions
May 9, 2026Le-Tuyet-Nhi Pham, Giovanni Conforti, Zhenjie Ren +1Conditional Flow Matching Maximum Mean Discrepancy
SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization
May 9, 2026Yan Sun, Guoxia Wang, Jinle Zeng +6Large Language Model Pretraining Model Pretraining
OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control
May 8, 2026Darryl Jacob, Xinyu Liu, Muchao Ye +2Inference-Time Control Decisions
Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
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The Geometric Structure of Models Learning Sparse Data
May 8, 2026Thomas Walker, T. Mitchell Roddenberry, Ahmed Imtiaz Humayun +2Feature Learning Deep Network
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May 8, 2026Ahmad Aloradi, Tim Roith, Emanuël A. P. Habets +1Sparsity Regularization Techniques
KL for a KL: On-Policy Distillation with Control Variate Baseline
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When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains
May 8, 2026Brandon Yee, Pairie Koh, Jack Rodriguez +1Partial Differential Equation Benchmarks Fourier
Beyond Defenses: Manifold-Aligned Regularization for Intrinsic 3D Point Cloud Robustness
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Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training
May 8, 2026Chen Wang, Hexuan Deng, Yining Zhang +5Reasoning Traces Regularization Techniques
Theoretical Limits of Language Model Alignment
May 8, 2026Lucas Monteiro Paes, Natalie Mackraz, Barry-John Theobald +1Large Language Model Alignment Bregman Divergences
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May 7, 2026Di Wu, Chengshuai Shi, Jing Yang +1Bregman Divergences Reinforcement Learning From Human Feedback
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May 7, 2026David Antrobius, Shangtong ZhangTemporal Difference Clocks
Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less
May 7, 2026Yuxing Liu, Jianyu Wang, Tong ZhangSupervised Fine-Tuning Model Pretraining
Beyond Negative Rollouts: Positive-Only Policy Optimization with Implicit Negative Gradients
May 7, 2026Mingwei Xu, Hao FangReinforcement Learning With Verifiable Reward Verifiable Rewards
Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization
May 7, 2026Abhijit Das, Sayantan DuttaLoss Landscape Regularization Techniques
Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance
May 7, 2026Gal Vinograd, Idan Achituve, Ethan FetayaDiversity Divergence
eXplaining to Learn (eX2L): Regularization Using Contrastive Visual Explanation Pairs for Distribution Shifts
May 7, 2026Paulo Mario P. Medina, Jose Marie Antonio Miñoza, Sebastian C. IbañezExplainability Distribution Shifts

May 13, 2026 · cs.LGJ/K move · Enter open · S save

ERPPO: Entropy Regularization-based Proximal Policy Optimization

Changha Lee, Gyusang Cho

Korea Advanced Institute of Science and Technology, Daejeon, South Korea

Multi-Agent Proximal Policy Optimization (MAPPO) is a variant of the Proximal Policy Optimization (PPO) algorithm, specifically tailored for multi-agent reinforcement learning (MARL). MAPPO optimizes cooperative multi-agent settings by employing a centralized critic with decentralized actors. However, in case of multi-dimensional environment, MAPPO can not extract optimal policy due to non-stationary agent observation. To overcome this problem, we introduce a novel approach, Entropy Regularization-based Proximal Policy Optimization (ERPPO). For the policy optimization, we first define the object detection ambiguity under multi-dimensional observation environment. Distributional Spatiotemporal Ambiguity (DSA) learner is trained to estimate object detection uncertainty in non-stationary constraints. Then, we enhance PPO with a novel Entropy Regularization term. This regularization dynamically adjusts the policy update by applying a stronger (L1) regularization in high-ambiguity observation to encourage significant exploratory actions and a weaker (L2) regularization in low-ambiguity observation to stabilize the proximal policy optimization. This approach is designed to enhance the probability of successful object localization in time-critical operations by reducing detection failures and optimizing search policy. Experiments on a testbed with AirSim-based maritime searching scenarios show that the proposed ERPPO improves accuracy performance. Our proposed method improves higher gradient than MAPPO. Qualitative results confirm that ERPPO effectiveness in terms of suppressing false detection in visually uncertain conditions.

Proximal Policy Optimization Multi-Agent Reinforcement Learning Entropy-Regularized Equilibrium Selection Object Detection

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