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A Survey of In-Context Reinforcement Learning

arXiv 2502.07978 ↗First posted 11 Feb 2025cs.LGcited by 35

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Abstract (arXiv)

Reinforcement learning (RL) agents typically optimize their policies by performing expensive backward passes to update their network parameters. However, some agents can solve new tasks without updating any parameters by simply conditioning on additional context such as their action-observation histories. This paper surveys work on such behavior, known as in-context reinforcement learning.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2025.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 19; 2026: 16.

35 papers.

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  1. 2025A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligencecited by 155
  2. 2025A Survey on Vision-Language-Action Models: An Action Tokenization Perspectivecited by 96
  3. 2025Reward Is Enough: LLMs Are In-Context Reinforcement Learnerscited by 29
  4. 2025LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacracited by 20
  5. 2025A Survey of Continual Reinforcement Learningcited by 16
  6. 2025Yes, Q-learning Helps Offline In-Context RLcited by 8
  7. 2025Scalable In-Context Q-Learningcited by 5
  8. 2026Complementary RL: Towards Efficient Experience-Driven Agent Learningcited by 5
  9. 2025Experience Replay Addresses Loss of Plasticity in Continual Learningcited by 4
  10. 2026Improving Efficiency of GPU Kernel Optimization Agents using a Domain-Specific Language and Speed-of-Light Guidancecited by 4
  11. 2025Scalable Strategies for Continual Learning with Replaycited by 3
  12. 2025In-Context Reinforcement Learning via Communicative World Modelscited by 3
23 more
  1. 2025When Greedy Wins: Emergent Exploitation Bias in Meta-Bandit LLM Trainingcited by 3
  2. 2026Aligned, Orthogonal or In-conflict: When can we safely optimize Chain-of-Thought?cited by 3
  3. 2025Offline RLAIF: Piloting VLM Feedback for RL via SFOcited by 2
  4. 2025Towards Agentic OS: An LLM Agent Framework for Linux Schedulerscited by 2
  5. 2025Discovering New Theorems via LLMs with In-Context Proof Learning in Leancited by 2
  6. 2025Towards Provable Emergence of In-Context Reinforcement Learningcited by 2
  7. 2025Safe In-Context Reinforcement Learningcited by 2
  8. 2026Reasoning aligns language models to human cognitioncited by 2
  9. 2026Hierarchical Experimentalist Agentscited by 2
  10. 2025ViTSP: A Vision Language Models Guided Framework for Solving Large-Scale Traveling Salesman Problemscited by 1
  11. 2025Prompt-Driven Domain Adaptation for End-to-End Autonomous Driving via In-Context RLcited by 1
  12. 2025Long-Horizon Model-Based Offline Reinforcement Learning Without Explicit Conservatismcited by 1
  13. 2026Hallucination-Resistant Security Planning with a Large Language Modelcited by 1
  14. 2026IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agentscited by 1
  15. 2026Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvementcited by 1
  16. 2026In-Context Reinforcement Learning through Bayesian Fusion of Context and Value Priorcited by 0
  17. 2026TR-ICRL: Test-Time Rethinking for In-Context Reinforcement Learningcited by 0
  18. 2026Bridging Natural Language and Microgrid Dynamics: A Context-Aware Simulator and Datasetcited by 0
  19. 2026When Do We Need LLMs? A Diagnostic for Language-Driven Banditscited by 0
  20. 2026Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learningcited by 0
  21. 2026From Weak Data to Strong Policy: Q-Targets Enable Provable In-Context Reinforcement Learningcited by 0
  22. 2026From Static Policies to Adaptive Priors in Offline Reinforcement Learningcited by 0
  23. 2026From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMscited by 0