Contrastive RL

RL: Reinforcement Learning

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 18

All topics

Direction-Conditioned Policies for Online Goal-Conditioned Reinforcement Learning
Oct 4, 2026S K Swaminathan, Damiya Gondha, Theyanesh Eswaramoorthy Rajahkrishnan +1Reinforcement Learning Contrastive RL
Department of Computer Science and Engineering Indian Institute of Technology Kharagpur, India · Department of Mechanical Engineering Indian Institute of Technology Kharagpur, India
Learning to Zoom Efficiently with a Contrastive Curriculum
Sep 2, 2026Falko Helm, Iryna GurevychTool Use in VLMs Contrastive RL
Ubiquitous Knowledge Processing Lab (UKP Lab) Department of Computer Science and Hessian Center for AI (hessian.AI) Technical University of Darmstadt
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
Contrastive Reinforced Policy Optimization via Privileged Self-Distillation
Jul 30, 2026Xingjian Wu, Junlin Liu, Xingchen Liu +6Agentic RL RL for Language Model Reasoning
Meituan
Good Rankers, Bad Objectives: Bilinear Contrastive Critics under Expressive Policy Search
Jul 29, 2026Ayushman Singh, Siddharth AphaleLearning to Rank Contrastive RL
Stanford University
RRPO: Reference-Relative Policy Optimization with Stratified Conditional Rollouts
Jul 20, 2026Yuxin Xiong, Xunyi Jiang, Rohan Surana +8Reinforcement Learning RL for Language Model Reasoning
University of California San Diego · 2Adobe Research
GCT-MARL: Graph-Based Contrastive Transfer for Sample-Efficient Cooperative Multi-Agent Reinforcement Learning
Jun 23, 2026Animesh Animesh, Satheesh K Perepu, Kaushik DeyGraph Contrastive Learning Contrastive RL
Department of Artificial Intelligence, Indian Institute of Technology Kharagpur, India · Work done during research internship at Ericsson Research, Bangalore, India · Ericsson Research, Bangalore, India
Context-Aware RL for Agentic and Multimodal LLMs
Jun 15, 2026Peiyang Xu, Bangzheng Li, Sijia Liu +4Multimodal Grounding Contrastive RL
1Princeton University · 2UC Davis
Direction-Conditioned Policies via Compositional Subgoal Scoring for Online Goal-Conditioned Reinforcement Learning
Jun 15, 2026Swaminathan S K, Damiya Gondha, Theyanesh Eswaramoorthy Rajahkrishnan +1Contrastive RL Policy Learning
Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India · Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India.
Learning Object Manipulation from Scratch via Contrastive Interaction
Jun 10, 2026Tongle Shen, Caleb Chuck, Fan Feng +1Dynamical Systems Reinforcement Learning
1UC San Diego · 2UT Austin
Harmony in Diversity: Multi-domain Contrastive Policy Optimization for Large Reasoning Models
May 25, 2026Zongji Yu, Wenshui Luo, Yiliu Sun +5Reinforcement Learning RL for Language Model Reasoning
Shanghai Jiao Tong University · Shanghai AI Laboratory · Shandong University
Self-Supervised On-Policy Reinforcement Learning via Contrastive Proximal Policy Optimisation
May 13, 2026Asim Osman, Sasha Abramowitz, Mark Bergh +13Reinforcement Learning Contrastive RL
InstaDeep, AIMS · InstaDeep, University of Stellenbosch
Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective
May 13, 2026Feng Zhang, Xinhong Ma, Ziqiang Dong +5RL for Language Model Reasoning Group Relative Policy Optimization
Beijing Institute of Technology · Qwen Business Unit of Alibaba · The Chinese University of Hong Kong, Shenzhen +1
From Generic Correlation to Input-Specific Credit in On-Policy Self Distillation
May 12, 2026Guobin Shen, Lei Huang, Xiang Cheng +4Reinforcement Learning On-Policy Self-Distillation
Xiaohongshu Inc. · Institute of Automation, Chinese Academy of Sciences
PRTS: A Primitive Reasoning and Tasking System via Contrastive Representations
Apr 30, 2026Yang Zhang, Jiangyuan Zhao, Chenyou Fan +11Reinforcement Learning Long-Horizon Robotic Manipulation
Institute of Artificial Intelligence (TeleAI), China Telecom · Tsinghua University · Shanghai Jiao Tong University +1
Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions
Feb 7, 2026Yicheng Yang, Ruijiao Li, Lifeng Wang +7Reinforcement Learning Robotic RL
CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning
Jul 18, 2025Xiaoya Li, Albert Wang, Guoyin Wang +2Reinforcement Learning GPU Kernel Optimization
DeepReinforce Team
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 Robotic RL
Princeton University · Warsaw University of Technology · Warsaw University of Technology, Tooploox, IDEAS Research Institute

Oct 4, 2026, cs.LGJ/K move · Enter open · S save

Direction-Conditioned Policies for Online Goal-Conditioned Reinforcement Learning

S K Swaminathan, Damiya Gondha, Theyanesh Eswaramoorthy Rajahkrishnan, Aritra Hazra

Department of Computer Science and Engineering Indian Institute of Technology Kharagpur, India · Department of Mechanical Engineering Indian Institute of Technology Kharagpur, India

Reinforcement Learning Contrastive RL Goal-Conditioned RL

Open paper AskPDF arXiv

Contrastive Reinforcement Learning (CRL) learns representations that estimate goal reachability, yet its policy remains conditioned on raw goals and therefore does not directly exploit the geometry encoded by its critic. We introduce Direction-Conditioned Policies (DCP), a method built around a small modification to CRL: DCP selects previously visited states as waypoints during online training and conditions the policy on their direction and distance in representation space. At deployment, DCP applies the same interface directly to the final goal, requiring neither waypoint selection nor planning. Across nine navigation and manipulation tasks, DCP attains higher final success rates than CRL on seven tasks and spends more time near the goal on seven. Controlled maze experiments further show that DCP captures shortest-path geometry more accurately and that the supplied direction causally influences the actor's behavior. We identify waypoint coverage and ranking as limits to exploration, and show that learned candidate generation improves goal reaching in two controlled mazes.

Momentum

Related topics

Latest papers 18