Dense Reward RL

RL: Reinforcement Learning

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs
Sep 11, 2026Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang +1Reinforcement Learning Hierarchical RL
School of Computer Science, McGill University Mila – Quebec AI Institute · Google DeepMind, CIFAR Fellow
Long-Horizon Language Model Reinforcement Learning via Progressive Point Matching
Sep 7, 2026Preston Fu, Kevin Frans, Oleh Rybkin +2Reinforcement Learning Reward Shaping
UC Berkeley · Carnegie Mellon University
Dense Process Supervision for Search Agents via Fact Utility Estimation
Sep 1, 2026Rongzhi Zhu, Xiangyu Liu, Yi Liu +7Reinforcement Learning Credit Assignment in RL
State Key Laboratory for Novel Software Technology, Nanjing University, China · Ant Group, China · National Institute of Healthcare Data Science, Nanjing University, China
DHRCL:Training Code LLMs with Dense Hierarchical Rewards and Curriculum Learning
Jul 29, 2026Shuhang Wang, Ziming Li, Hui ChengRL for Code Generation Reinforcement Learning
The Dark Room in the Reward Channel: Dense Prediction Rewards Collapse GRPO-Trained LLM Agents -- and The Channel, Not the Content, Decides What Works
Jul 23, 2026Yu WangReward Hacking Reward Shaping
Independent researcher
Lyapunov Exponent as Physics-Informed Dense Reward: RL Discovery of Stabilization Beyond the Kapitza Pendulum
Jul 15, 2026Slava AndrejevRL Control Reinforcement Learning
TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents
Jul 15, 2026Leitian Tao, Baolin Peng, Wenlin Yao +5Credit Assignment in RL Step-Level Credit Assignment in RL
University of Wisconsin–Madison · Microsoft Research
Stage-Transition Dense Reward Modeling for Reinforcement Learning
Jun 30, 2026Yang Yang, Bingjie Chen, Zihan Wang +4RL for Robotics Long-Horizon Robotic Manipulation
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China. · Zerith Robotics, Shenzhen 518055, China.
Learning Process Rewards via Success Visitation Matching for Efficient RL
Jun 22, 2026Raymond Tsao, Andrew Wagenmaker, Sergey LevineProcess Reward Models Credit Assignment in RL
UC Berkeley
RARM: Confidence-Gated Progress Reward Modeling for RL in Manipulation
Jun 20, 2026Pengzhi Yang, Xinyu Wang, Pengyu Jing +7Long-Horizon Robotic Manipulation Robot Skill Learning
† NUS Human-Centered Robotic Lab · ‡ Booking.com · § University of Cambridge +1
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
Domain-Adaptable Reinforcement Learning for Code Generation with Dense Rewards
May 20, 2026Erfan Aghadavoodi Jolfaei, Daniel Maninger, Abhinav Anand +2RL for Code Generation Reinforcement Learning
Technische Universität Darmstadt, Darmstadt, Germany · Hessian Center for Artificial Intelligence (hessian.AI), Darmstadt, Germany · National Research Center for Applied Cybersecurity ATHENE, Darmstadt, Germany.
Generalizable Dense Reward for Long-Horizon Robotic Tasks
Mar 31, 2026Silong Yong, Stephen Sheng, Carl Qi +6Reinforcement Learning Long-Horizon Robotic Manipulation
Carnegie Mellon University · Amazon Robotics · UT Austin
SpatialThinker: Reinforcing Scene Graph-Grounded Spatial Reasoning via Dense Rewards
Nov 10, 2025Hunar Batra, Haoqin Tu, Hardy Chen +3Visual Question Answering Scene Graph Generation
University of Oxford · University of California, Santa Cruz

Sep 11, 2026, cs.LGJ/K move · Enter open · S save

From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs

Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang, Doina Precup

School of Computer Science, McGill University Mila – Quebec AI Institute · Google DeepMind, CIFAR Fellow

Reinforcement Learning Hierarchical RL Dense Reward RL Reward Design for RL Goal-Conditioned RL

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The integration of graphs with Goal-Conditioned Hierarchical Reinforcement Learning (GCHRL) has received increasing attention, as graphs naturally encode task hierarchies for effective subgoal sampling. However, existing methods often overlook intrinsic connectivity information, failing to fully leverage the underlying topology for efficient learning. Most graph-based GCHRL methods use the graph as a stochastic sampling tool rather than as an environmental model that encodes connectivity and state-accessibility information. This limitation is particularly acute in quasimetric environments, where the inherent asymmetry of state transitions poses a fundamental challenge to stable policy learning and robust path planning. In this paper, we address these problems by introducing a state connectivity model designed to predict pairwise state connectivity strength in asymmetric environments. We transform these connectivity strengths into scalar auxiliary dense rewards, providing continuous guidance across multiple hierarchical levels. We demonstrate that our proposed framework, Graph-Guided Quasimetric Dense Reward (G2QDR), can theoretically be integrated into any existing GCHRL architecture, and the state connectivity model is efficiently implemented via a neural network trained on a directed state graph generated during exploration. Empirical results across a wide range of sparse reward environments indicate that, in general, G2QDR can enhance the performance of baseline GCHRL approaches with acceptable computational overhead.

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