Sparse-Reward RL

RL: Reinforcement Learning

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19 papers in the last four weeks, up 138% on the four weeks before. 0.2% of all new papers.

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MSPR: Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning
May 10, 2026Valliappan Chidambaram Adaikkappan, Sai Rajeswar, Pietro Mazzaglia +1Offline RL Predictive Representation Learning
Mila, McGill University · Qualcomm Research
Functional Graphs for Predicting and Explaining Goal Failure in Sparse Goal-Conditioned RL
May 10, 2026Shalley DashReinforcement Learning Sparse-Reward RL
Associate Adjunct Professor, Business Analytics, Institute of Management Technology Ghaziabad, 201001
PiCA: Pivot-Based Credit Assignment for Search Agentic Reinforcement Learning
May 10, 2026Dongyi Liu, Yifan Niu, Qinwen Wang +2Reinforcement Learning Reward Shaping
The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology
Learning CLI Agents with Structured Action Credit under Selective Observation
May 8, 2026Haoyang Su, Ying WenComputer-Use Agents Credit Assignment in RL
Fudan University · Shanghai Innovation Institute · Shanghai Jiao Tong University
Soft Deterministic Policy Gradient with Gaussian Smoothing
May 7, 2026Hyunjun Na, Donghwan LeeRL Control Policy Gradient
School of Electrical Engineering, KAIST 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
May 3, 2026Xing Lei, Jincheng Wang, Xuetao Zhang +1Offline RL Sparse-Reward RL
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · Computer Science, University College London · School of Engineering, Westlake University
Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach
May 3, 2026Yang Fu, Peng Qin, Liming Chen +3Reward Shaping Cost-Aware Inference
Yang Fu, Peng Qin, Member, IEEE, Liming Chen, Zihao Zhang, Hao Yu, and Yifei Wang
$S^3$ -R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data
May 2, 2026Harsh Goel, Akhil Udathu, Susmija Jabbireddy +2Multi-Hop QA Synthetic Data Generation
The University of Texas at Austin · Work done during an internship at Google DeepMind · Google DeepMind
AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning
May 1, 2026Haotian Zhao, Songlin Zhou, Yuxin Zhang +9Agentic RL Credit Assignment in RL
Baidu · Tsinghua University · Fudan University
Sample-efficient Neuro-symbolic Proximal Policy Optimization
Apr 28, 2026Simone Murari, Celeste Veronese, Daniele MeliReinforcement Learning Proximal Policy Optimization
Dept. of Computer Science, University of Verona, Italy
Zero Shot Coordination for Sparse Reward Tasks with Diverse Reward Shapings
Apr 28, 2026Keenan Powell, Peihong Yu, Pratap TokekarMulti-Agent Coordination Reward Shaping
Department of Computer Science University of Maryland, College Park
ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation
Apr 24, 2026Peiyan Zhang, Hanmo Liu, Chengxuan Tong +3RL for Generative Models Sparse-Reward RL
1Huawei Technologies Co., Ltd.
Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning
Apr 22, 2026Aravind Venugopal, Jiayu Chen, Xudong Wu +3Reward Shaping Temporal Credit Assignment
Carnegie Mellon University · The University of Hong Kong · INFIFORCE Intelligent Technology +1
Reducing Credit Assignment Variance via Counterfactual Reasoning Paths
Apr 20, 2026Fei Ding, Yongkang Zhang, Youwei Wang +1RL for Language Model Reasoning Credit Assignment in RL
Alibaba Group · Tsinghua University
P^2O: Joint Policy and Prompt Optimization
Mar 23, 2026Xinyu Lu, Kaiqi Zhang, Jinglin Yang +6RL for Language Model Reasoning Reinforcement Learning with Verifiable Rewards
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Institute of Information Engineering, Chinese Academy of Sciences +2
Hindsight-Anchored Policy Optimization: Learning Through Hindsight with Thompson Sampling-Inspired Adaptive Gating
Mar 11, 2026Yuning Wu, Ke Wang, Haoran Liu +3Reinforcement Learning RL for Language Model Reasoning
Amazon
TSR: Trajectory-Search Rollouts for Multi-Turn RL of LLM Agents
Feb 12, 2026Aladin Djuhera, Swanand Kadhe, Farhan Ahmed +3Tree Search LLM Agent Training
Technical University Munich · IBM Research
Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability
Jan 26, 2026Shobhita Sundaram, John Quan, Ariel Kwiatkowski +3Self-Play RL RL for Language Model Reasoning
1MIT · 2Meta FAIR · 3New York University
BaNEL: Exploration Posteriors for Generative Modeling Using Only Negative Rewards
Oct 10, 2025Sangyun Lee, Brandon Amos, Giulia FantiGenerative Modeling RL for Generative Models
Carnegie Mellon University
Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration
Sep 12, 2025Chirayu Nimonkar, Shlok Shah, Catherine Ji +1Multi-Agent Coordination RL Exploration
Princeton University
CONTHER: Context-Aware Reinforcement Learning for Robotic Manipulation with Sparse Rewards
Mar 20, 2025Maria Makarova, Qian Liu, Dzmitry TsetserukouReinforcement Learning Experience Replay
Intelligent Space Robotics Laboratory, Center for Digital Engineering, Skolkovo Institute of Science and Technology (Skoltech), 121205 Moscow, Russia · Department of Computer Science and Technology, Dalian University of Technology, China

May 10, 2026, cs.LGJ/K move · Enter open · S save

MSPR: Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning

Valliappan Chidambaram Adaikkappan, Sai Rajeswar, Pietro Mazzaglia, David Meger

Mila, McGill University · Qualcomm Research

Offline RL Predictive Representation Learning State Representation Learning Sparse-Reward RL Goal-Conditioned RL

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This paper investigates robust representation learning in offline goal-conditioned reinforcement learning (GCRL). Particularly in sparse reward scenarios, learning representations that align state and goal latents is a challenge, as the encoder can learn goal-agnostic features that destabilize policy learning. We address this issue by learning the encoder's representation with alignment objectives that capture the environment across multiple scales, from local physical dynamics to long-horizon goal-directed structure. Concretely, we propose MSPR, a framework that leverages multi-scale predictive supervision to enforce goal-directed alignment within the latent space. We demonstrate that MSPR leads to strong performance on both vision and state-based tasks. Furthermore, we show that our approach is resilient under realistic, challenging data regimes, maintaining state-of-the-art performance across a wide variety of tasks.

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