# A Survey of In-Context Reinforcement Learning

arXiv: https://arxiv.org/abs/2502.07978 · HTML: https://mlsanity.com/paper/2502.07978/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- First posted on arXiv 11 Feb 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Built on by (most cited first, 30 of 39)

- [A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence](https://mlsanity.com/paper/2507.21046/) (2025)
- [A Survey on Vision-Language-Action Models: An Action Tokenization Perspective](https://mlsanity.com/paper/2507.01925/) (2025)
- [Reward Is Enough: LLMs Are In-Context Reinforcement Learners](https://mlsanity.com/paper/2506.06303/) (2025)
- [LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra](https://mlsanity.com/paper/2507.15815/) (2025)
- [A Survey of Continual Reinforcement Learning](https://mlsanity.com/paper/2506.21872/) (2025)
- [Yes, Q-learning Helps Offline In-Context RL](https://mlsanity.com/paper/2502.17666/) (2025)
- [Scalable In-Context Q-Learning](https://mlsanity.com/paper/2506.01299/) (2025)
- [In-Context Reinforcement Learning via Communicative World Models](https://mlsanity.com/paper/2508.06659/) (2025)
- [Complementary RL: Towards Efficient Experience-Driven Agent Learning](https://mlsanity.com/paper/2603.17621/) (2026)
- [Improving Efficiency of GPU Kernel Optimization Agents using a Domain-Specific Language and Speed-of-Light Guidance](https://mlsanity.com/paper/2603.29010/) (2026)
- [Aligned, Orthogonal or In-conflict: When can we safely optimize Chain-of-Thought?](https://mlsanity.com/paper/2603.30036/) (2026)
- [Experience Replay Addresses Loss of Plasticity in Continual Learning](https://mlsanity.com/paper/2503.20018/) (2025)
- [Scalable Strategies for Continual Learning with Replay](https://mlsanity.com/paper/2505.12512/) (2025)
- [Towards Provable Emergence of In-Context Reinforcement Learning](https://mlsanity.com/paper/2509.18389/) (2025)
- [When Greedy Wins: Emergent Exploitation Bias in Meta-Bandit LLM Training](https://mlsanity.com/paper/2509.24923/) (2025)
- [Safe In-Context Reinforcement Learning](https://mlsanity.com/paper/2509.25582/) (2025)
- [Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement](https://mlsanity.com/paper/2605.05755/) (2026)
- [Offline RLAIF: Piloting VLM Feedback for RL via SFO](https://mlsanity.com/paper/2503.01062/) (2025)
- [Towards Agentic OS: An LLM Agent Framework for Linux Schedulers](https://mlsanity.com/paper/2509.01245/) (2025)
- [Discovering New Theorems via LLMs with In-Context Proof Learning in Lean](https://mlsanity.com/paper/2509.14274/) (2025)
- [Reasoning aligns language models to human cognition](https://mlsanity.com/paper/2602.08693/) (2026)
- [Hierarchical Experimentalist Agents](https://mlsanity.com/paper/2606.29315/) (2026)
- [ViTSP: A Vision Language Models Guided Framework for Solving Large-Scale Traveling Salesman Problems](https://mlsanity.com/paper/2509.23465/) (2025)
- [Prompt-Driven Domain Adaptation for End-to-End Autonomous Driving via In-Context RL](https://mlsanity.com/paper/2511.12755/) (2025)
- [Long-Horizon Model-Based Offline Reinforcement Learning Without Explicit Conservatism](https://mlsanity.com/paper/2512.04341/) (2025)
- [In-Context Reinforcement Learning through Bayesian Fusion of Context and Value Prior](https://mlsanity.com/paper/2601.03015/) (2026)
- [Hallucination-Resistant Security Planning with a Large Language Model](https://mlsanity.com/paper/2602.05279/) (2026)
- [Vintix II: Decision Pre-Trained Transformer is a Scalable In-Context Reinforcement Learner](https://mlsanity.com/paper/2604.05112/) (2026)
- [IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agents](https://mlsanity.com/paper/2604.05157/) (2026)
- [Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning](https://mlsanity.com/paper/2605.25267/) (2026)

## 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.