Intrinsic Reward Methods

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4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Latest papers 19

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When Do Intrinsic Rewards Lead to Exploration?
Oct 1, 2026Scott W. Viteri, Laura Gomezjurado Gonzalez, Clark BarrettIntrinsic Reward Methods RL Exploration
Stanford University
Belief-Based Maximum Occupancy Principle and Active Inference
Sep 30, 2026Manolis Mylonas, Rubén Moreno BoteBelief-Space Planning Intrinsic Reward Methods
Center for Brain and Cognition & Department of Engineering Universitat Pompeu Fabra Barcelona, Spain
Intrinsic Robot Rewarding: Reusing VLA Representations for Autonomous Evaluation and Policy Improvement
Sep 15, 2026Tobias Schaffer, Mohab Elkhayat, Daniela Nicklas +2RL for Robotics Intrinsic Reward Methods
Technology Campus Cham - Intelligent Robotics, Deggendorf Institute of Technology, Cham, Germany
Efficient Exploration Is Enough
Sep 7, 2026Mikel Malagón, Jon Vadillo, Josu Ceberio +2Intrinsic Reward Methods RL Exploration
University of the Basque Country (UPV/EHU) · University of Alberta · Alberta Machine Intelligence Institute +2
PAST: Prompt-Adaptive Sampling Termination for Efficient Diffusion Model
Aug 7, 2026Renye Yan, Jikang Cheng, You Wu +4Intrinsic Reward Methods Reinforcement Learning
Peking University Beijing, China
Search-G1: Grounded Search Agents via Representation-Based Intrinsic Rewards
Jul 24, 2026Cheng Ruoxi, Ma Haoxuan, Zhang Hongyi +7Retrieval-Augmented Generation LLM Grounding
1Fudan University · 2Tencent · 3Nanjing University +2
Principled Direction-Free Intrinsic Motivation through Model-Free Epistemic Free-Energy Estimators
Jul 18, 2026Alireza Furutanpey, Schahram DustdarFree Energy Calculation Intrinsic Reward Methods
Coovally, Barcelona, Spain · Distributed Systems Group, TU Vienna, Austria · ICREA Barcelona, Spain
Trust Your Instincts: Confidence-Driven Test-Time RL for Vision-Language-Action Models
Jun 29, 2026Siyao Chen, Jiakang Yuan, Jiaxin Wang +1Intrinsic Reward Methods Reinforcement Learning
College of Future Information Technology, Fudan University, Shanghai, China · Shanghai Innovation Institute, Shanghai, China · MoShen Intelligence, Shanghai, China
OPERA: Aligning Open-Ended Reasoning via Objective Perplexity-based Reinforcement Learning
Jun 24, 2026Wenxuan Jiang, Zining Fan, Zijian Zhang +6Open-Ended Generation Intrinsic Reward Methods
The Hong Kong Polytechnic University · Peking University · Meituan Longcat Team +2
When Do Intrinsic Rewards Work for Code Reasoning? A Comprehensive Study
Jun 18, 2026Xiaolong Jin, Xuandong Zhao, Wenbo Guo +2RL for Code Generation Intrinsic Reward Methods
Purdue University · UC Berkeley · UC Santa Barbara
Can In-Context Learning Support Intrinsic Curiosity?
Jun 17, 2026Eric Elmoznino, Sangnie Bhardwaj, Johannes von Oswald +5Intrinsic Reward Methods Intrinsic Motivation
1Google – Paradigms of Intelligence Team, 2Google DeepMind
Momentum for Reasoning: Dense Intrinsic Signals in Policy Optimization
Jun 7, 2026Hao Chen, Zhanming Shen, Liyao Li +8Intrinsic Reward Methods Reinforcement Learning
Zhejiang University · The Chinese University of Hong Kong · Eastern Institute of Technology
Two is better than one: A Collapse-free Multi-Reward RLIF Training Framework
May 21, 2026Shourov Joarder, Diganta Sikdar, Ahsan Habib Akash +2Multi-Objective Reinforcement Learning Intrinsic Reward Methods
Bangladesh University of Engineering and Technology, Bangladesh · West Virginia University, USA · University of Aberdeen, UK +2
CIG: Exploration via Conditional Information Gain
May 20, 2026Tim Joseph, Marcus Fechner, Philipp Stegmaier +2Intrinsic Reward Methods Intrinsic Motivation
FZI · KIT
Verifier-Free RL for LLMs via Intrinsic Gradient-Norm Reward
May 11, 2026Xuexiang Wen, Hang Yu, Linchao Zhu +1Intrinsic Reward Methods Reinforcement Learning
1Zhejiang University · 2Ant Group
Quality-Aware Exploration Budget Allocation for Cooperative Multi-Agent Reinforcement Learning
May 3, 2026Dahyun Oh, Minhyuk Yoon, H. Jin KimMulti-Agent Coordination Intrinsic Reward Methods
Department of Aerospace Engineering, Seoul National University, Seoul, Republic of Korea
MAGIC: Multi-Step Advantage-Gated Causal Influence for Multi-agent Reinforcement Learning
May 3, 2026Haohan Yu, Jinmiao Cong, Shengzhi Wang +2Multi-Agent Coordination Intrinsic Reward Methods
1Dalian University of Technology · 2Microsoft, Beijing, China
Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training
Apr 20, 2026Vin Bhaskara, Haicheng WangIntrinsic Reward Methods Reinforcement Learning
Department of Computer Science, University of Toronto, Toronto, Canada.
ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy
Dec 5, 2024Hongming Li, Zhao Yang, Xiaoxuan Liang +2Intrinsic Reward Methods Reinforcement Learning
Zhejiang Lab, Huangzhou, China · Vrije Universiteit Amsterdam, Amsterdam, the Netherlands · Computational NeuroEngineering Laboratory at the University of Florida, Gainesville, USA

Oct 1, 2026, cs.LGJ/K move · Enter open · S save

When Do Intrinsic Rewards Lead to Exploration?

Scott W. Viteri, Laura Gomezjurado Gonzalez, Clark Barrett

Stanford University

Intrinsic Reward Methods RL Exploration Reward Design for RL

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Intrinsic rewards are designed to guide exploration in reinforcement learning by assigning value to an agent's experience, for example through prediction error or learning progress. However, maximizing these rewards need not produce the most informative experience available. We propose a formal criterion for exploration that compares policies by the counterfactual information they acquire: how well their histories can substitute for experience under alternative policies. We construct a single, simple environment in which specified count-based, prediction-error, empowerment, and information-gain objectives have maximizing policies that are Pareto-suboptimal at acquiring counterfactual information. We explain these failures and establish conditions under which existing intrinsic rewards successfully encourage optimal exploration. We also construct an objective that assigns a higher value whenever exploration strictly improves under our criterion.

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