arXiv Machine Learning
Context Value Informed In-Context Reinforcement Learning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jul 27
Safe In-Context Reinforcement Learning
arXiv:2509. 25582v4 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks without any parameter updates, instead relying on an expanding context of interaction history.
arXiv AI
Jul 15
In-Context Reinforcement Learning under Non-Stationarity: A Survey
arXiv:2607. 11906v1 Announce Type: new Abstract: The development of decision-pretrained transformers, algorithm distillation, long-context meta-RL, and retrieval-augmented agents has renewed interest in in-context reinforcement learning (ICRL): the ability of a pretrained or fine-tuned decision model to infer latent task rules and improve future behavior from interaction context, without test-time parameter updates.
arXiv AI
Jul 16
Learning to Learn-at-Test-Time: Language Agents with Learnable Adaptation Policies
arXiv:2604. 00830v3 Announce Type: replace-cross Abstract: Test-Time Learning (TTL) enables language agents to iteratively refine their performance through repeated interactions with the environment at inference time.
arXiv Machine Learning
2d ago
METTA: Memory-Enhanced Test-Time Adaptation for Meta-Reinforcement Learning
METTA: Memory-Enhanced Test-Time Adaptation for Meta-Reinforcement Learning proposes a new approach that splits fast adaptation into two temporal scales: a causal neural memory for fine-grained within-episode adaptation with linear complexity, and compact episode-level summaries for long-range cross-episode inference. This design reduces the quadratic memory and computation costs of transformer-based agents while retaining expressive capacity for test-time training. Experiments on unseen Meta-World and PointMaze tasks show METTA achieves over 50% success on Meta-World and outperforms transformer baselines on long-context sparse-reward environments.
arXiv AI
Oct 15
TextualRL: Textual Reinforcement Learning for Context Management
TextualRL is a gradient‑free reinforcement learning framework that updates the textual context guiding large language model agents without altering their parameters. It treats groups of rollouts from the same task as units of critique, contrasting successful and failed runs to generate context patches, and validates these patches using held‑out rewards. Experiments on six benchmarks show that TextualRL boosts test accuracy by 24.4 and 15.3 percentage points for GPT‑5.5 and Qwen3.8‑27B, respectively.
Hugging Face Trending Papers
Oct 8
Do LLMs Learn from Rewards in Context? : Rethinking the role of reward in In-Context Reinforcement Learning
LLM agents increasingly improve at inference time by accumulating experience in context rather than by updating parameters. This process is often described as in-context reinforcement learning (ICRL)....