The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

Learning through interaction with an environment, receiving rewards or penalties to learn optimal behavior policies.

A scalar feedback signal indicating how good an action was, used to train reinforcement learning agents.

In RL, the learner or decision-maker that takes actions in an environment to maximize cumulative reward.

In RL, the world the agent interacts with, providing states, accepting actions, and returning rewards.

A mathematical framework for modeling sequential decision-making with states, actions, rewards, and transition probabilities.

The field of AI that lets computers read, interpret, translate and generate human language, from spam filters and search to chatbots.

The basic unit of text that a language model processes, typically representing a word, subword, or character. Tokens are the fundamental building blocks for LLM input and output.

Splitting text into tokens, usually subword pieces, and mapping each to an integer ID so a language model can process it.

Learning probability distributions over sequences of words to predict what comes next.

A collection of data examples used for training, validating, or testing machine learning models.

The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

Learning from unlabeled data to discover hidden patterns, structures, or relationships without explicit target outputs.

Learning representations from unlabeled data by creating supervised tasks from the data itself (masked prediction, contrastive learning).

Training a model on a large dataset (often self-supervised) before fine-tuning on specific tasks, enabling transfer learning.

A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.

A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

Learning useful features or representations of data automatically, rather than hand-crafting them.

A list of numbers (a vector) that represents a word, sentence, image or other item, learned so that similar items end up close together.

A technique that lets a neural network weigh every part of its input when producing each output, focusing on the parts most relevant at that step.

A neural network architecture, introduced in 2017, built from stacked self-attention and feed-forward layers; the basis of nearly every modern large language model.

A neural network, almost always a transformer, trained on vast amounts of text to predict the next token, which lets it write, answer, summarize and follow instructions.

The maximum number of tokens an LLM can process at once, including both input prompt and generated output. Also called context length.

LLMs learning to call external tools, APIs, or functions to extend capabilities beyond text generation (calculators, search, code execution).

A system where a large language model decides its own next steps in a loop: calling tools, reading the results, and continuing until the task is done.