mlsanityData to 7 Oct 2026

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

arXiv 2003.04960 ↗First posted 10 Mar 2020cs.LG cs.AI stat.MLcited by 350

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Cited by 23 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over the past three decades, learning in many domains still requires a large amount of interaction with the environment, which can be prohibitively expensive in realistic scenarios. To address this problem, transfer learning has been applied to reinforcement learning such that experience gained in one task can be leveraged when starting to learn the next, harder task. More recently, several lines of research have explored how tasks, or data samples themselves, can be sequenced into a curriculum for the purpose of learning a problem that may otherwise be too difficult to learn from scratch. In this article, we present a framework for curriculum learning (CL) in reinforcement learning, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals. Finally, we use our framework to find open problems and suggest directions for future RL curriculum learning research.

Key facts. Cited by 350 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 25; 2022: 49; 2023: 46; 2024: 58; 2025: 90; 2026: 82.

350 papers.

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  1. 2023LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learningcited by 1,338
  2. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 281
  3. 2021Curriculum Learning: A Surveycited by 257
  4. 2021A Survey of Zero-shot Generalisation in Deep Reinforcement Learningcited by 164
  5. 2025REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewardscited by 121
  6. 2021Replay-Guided Adversarial Environment Designcited by 108
  7. 2025Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoningcited by 89
  8. 2022Automated Reinforcement Learning (AutoRL): A Survey and Open Problemscited by 69
  9. 2025SRPO: A Cross-Domain Implementation of Large-Scale Reinforcement Learning on LLMcited by 67
  10. 2022In Defense of the Unitary Scalarization for Deep Multi-Task Learningcited by 65
  11. 2023How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligencecited by 55
  12. 2025Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replaycited by 52
38 more
  1. 2022Visuo-Tactile Transformers for Manipulationcited by 43
  2. 2025MobileRL: Online Agentic Reinforcement Learning for Mobile GUI Agentscited by 43
  3. 2024Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Taskscited by 40
  4. 2021Interesting Object, Curious Agent: Learning Task-Agnostic Explorationcited by 38
  5. 2025MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoningcited by 37
  6. 2022Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregationcited by 34
  7. 2025Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interactioncited by 34
  8. 2021Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimizationcited by 33
  9. 2022Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoningcited by 33
  10. 2022LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulationcited by 32
  11. 2025RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learningcited by 30
  12. 2024No Regrets: Investigating and Improving Regret Approximations for Curriculum Discoverycited by 29
  13. 2022Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptationcited by 28
  14. 2022Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workerscited by 28
  15. 2023ASP: Learn a Universal Neural Solver!cited by 27
  16. 2024Matching 2D Images in 3D: Metric Relative Pose from Metric Correspondencescited by 25
  17. 2025LADDER: Self-Improving LLMs Through Recursive Problem Decompositioncited by 24
  18. 2024Adaptive teachers for amortized samplerscited by 23
  19. 2025DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Modelcited by 23
  20. 2023Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputscited by 22
  21. 2024Practice Makes Perfect: Planning to Learn Skill Parameter Policiescited by 22
  22. 2021A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learningcited by 21
  23. 2022A Deep Reinforcement Learning Framework For Column Generationcited by 21
  24. 2024Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learningcited by 21
  25. 2024DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RLcited by 20
  26. 2025How to Train Your Advisor: Steering Black-Box LLMs with Advisor Modelscited by 20
  27. 2022Compositional Multi-Object Reinforcement Learning with Linear Relation Networkscited by 19
  28. 2022CLUTR: Curriculum Learning via Unsupervised Task Representation Learningcited by 19
  29. 2023Proximal Curriculum for Reinforcement Learning Agentscited by 19
  30. 2024CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Modelscited by 19
  31. 2023Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challengescited by 17
  32. 2023CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labelscited by 17
  33. 2025Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPOcited by 17
  34. 2022Grounding Aleatoric Uncertainty for Unsupervised Environment Designcited by 16
  35. 2022Understanding the Complexity Gains of Single-Task RL with a Curriculumcited by 16
  36. 2023Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agentscited by 16
  37. 2022Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculumcited by 15
  38. 2022State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersectionscited by 15

50 most cited of 350 All 350 (JSON)