Self-Supervised RL

RL: Reinforcement Learning

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13 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

DeepWaveRL: Self-Supervised Full Waveform Inversion via Reinforcement Learning
Nov 2, 2026Peng Jin, Yinan Feng, Yinpeng Chen +2Inverse Problems Self-Supervised RL
University of North Carolina, Chapel Hill, U.S. · Google DeepMind, U.S. · Pennsylvania State University, U.S.
Learning to Group and Order: Cross-Instance Self-Supervised RL for Vision-Centric MLLMs
Oct 31, 2026Songming Yang, Hong-Tao Yu, Xiu-Shen WeiRL for VLMs Self-Supervised RL
School of Computer Science and Engineering, Southeast University · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University
DT-JEPA: Learning to Predict Future Representations for Decision-Making
Oct 23, 2026Changxu Ni, Yiyang Ni, Jun Li +4Transformer-Based RL Self-Supervised RL
Southeast University · Jiangsu Second Normal University · The University of Hong Kong +1
Self-Improving Video-to-Action Models
Oct 16, 2026Hyunwoo Ryu, Eric Ming Chen, Max Wilcoxson +1Self-Improving Agents Robot Learning from Video
Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139, USA
TSRL: Trigger-based Supervised Reinforcement Learning for Continual Agent Training
Oct 13, 2026Yuqian Fu, Kaiwen Jiang, Zijun Song +3Self-Supervised RL Continual Learning for LLM Agents
SKL-MAIS, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · University of Science and Technology of China
ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learning
Sep 28, 2026Nico Bohlinger, Jan PetersRL for Robotics Representation Learning
Technical University of Darmstadt, Germany · Robotics Institute Germany (RIG); German Research Center for AI (DFKI); hessian.AI
Three Steps at a Time: Learning Representations from Action Sequences in Contrastive RL
Aug 31, 2026Michal Korniak, Kamil Dybek, Benjamin Eysenbach +2Reinforcement Learning Offline RL
ETH Zurich · University of Warsaw · Princeton University +3
Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources
Jun 23, 2026Haoyuan Deng, Yihong Zhou, Thomas Morstyn +1Reinforcement Learning Self-Supervised RL
The University of Hong Kong, Hong Kong SAR, China · University of Oxford, Oxford, U.K. · The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, China
Survival Reinforcement Learning: Toward Scalable Self-Supervised RL
May 29, 2026Franki Nguimatsia-Tiofack, Fabian Schramm, Théotime Le Hellard +1Reinforcement Learning Self-Supervised RL
Inria and École Normale Supérieure PSL Research University Paris, France
Self-Supervised On-Policy Reinforcement Learning via Contrastive Proximal Policy Optimisation
May 13, 2026Asim Osman, Sasha Abramowitz, Mark Bergh +13Reinforcement Learning Self-Supervised RL
InstaDeep, AIMS · InstaDeep, University of Stellenbosch
TRIMMER: A New Paradigm for Video Summarization through Self-Supervised Reinforcement Learning
May 3, 2026Pritam Mishra, Coloma Ballester, Dimosthenis KaratzasReinforcement Learning Video Summarization
Universitat Pompeu Fabra, Barcelona, Spain · Universitat Autònoma de Barcelona, Barcelona, Spain
Waking Up Blind: Cold-Start Optimization of Supervision-Free Agentic Trajectories for Grounded Visual Perception
Apr 19, 2026Ashutosh Bajpai, Tamal Majumder, Akshay Nambi +1Reinforcement Learning Self-Supervised RL
Indian Institute of Technology Delhi, India · Microsoft Research, India · Indian Institute of Technology Abu Dhabi, UAE
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
Mar 19, 2025Kevin Wang, Ishaan Javali, Michał Bortkiewicz +2Reinforcement Learning Self-Supervised RL
Princeton University · Warsaw University of Technology · Warsaw University of Technology, Tooploox, IDEAS Research Institute

Nov 2, 2026J/K move · Enter open · S save

DeepWaveRL: Self-Supervised Full Waveform Inversion via Reinforcement Learning

Peng Jin, Yinan Feng, Yinpeng Chen, Youzuo Lin+1

University of North Carolina, Chapel Hill, U.S. · Google DeepMind, U.S. · Pennsylvania State University, U.S.

Inverse Problems Self-Supervised RL Reinforcement Learning

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Full Waveform Inversion (FWI) is a fundamental inverse problem in geophysics to estimate subsurface properties, such as velocity, from seismic measurements. Recently, self-supervised learning methods remove the need for ground-truth velocity maps during training by leveraging a forward operator to simulate seismic data from predictions and backpropagate the gradients of reconstruction loss through it. In this work, we consider a more challenging setting without accessing such gradients. To this end, we incorporate reinforcement learning into self-supervised FWI and propose a framework named DeepWaveRL. Specifically, we reformulate FWI as a policy learning problem, where the policy network predicts velocity maps as actions, and a forward operator is used solely for simulation. A global scalar reward is computed based on data misfit for each prediction and used to weight policy gradients. Thus, learning proceeds without accessing the gradients from the forward operator. Given the indirect and coarse supervision signals, we further identify key strategies to stabilize learning and mitigate the spatial credit assignment bottleneck. Experimental results show that, despite relying entirely on a single scalar reward, DeepWaveRL achieves competitive performance compared to supervised and self-supervised baselines. These findings suggest that our framework offers a flexible and complementary learning paradigm for the FWI research community.