Landmark 2 stops to get here · leads to 3

Reward

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

Your route here

2 stops · basics first
  1. Machine Learning ✓ understood

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

  2. Reinforcement Learning ✓ understood

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

  3. Reward · you are here ✓ understood

Picture it

  1. 01 Agent acts Takes action a in state s
  2. 02 Scalar reward r One number: how good that step was
  3. 03 Return G Sum of future rewards, discounted by γ
  4. 04 Policy update Shift toward actions with higher return
Notice that the reward is just one number per step; the agent learns by maximizing the sum of rewards over time, not any single one.

Where it sits

Before this

Reinforcement Learning
Reward

Leads to

Markov Decision Process Value Function Inverse Reinforcement Learning

Explore nearby

Agents & RL Value Function A function estimating expected cumulative reward from a state (state-value) or state-action pair (action-value/Q-value). Agents & RL Policy A strategy or mapping from states to actions that defines the agent's behavior in reinforcement learning. Agents & RL Environment In RL, the world the agent interacts with, providing states, accepting actions, and returning rewards. Language & LLMs RLHF Reinforcement Learning from Human Feedback - training models using human preferences to align behavior with human values. Agents & RL Inverse Reinforcement Learning Learning reward functions from expert demonstrations, inferring what is being optimized.

In the research

All papers →

5 papers that build on Reward .

Frontier · Jan 2026 · 114 citations GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization Multi-objective RL is the norm in post-training; this fixes a subtle flaw in the default algorithm. Frontier · Aug 2025 · 112 citations On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification A tiny, theory-backed change to the most common training step in the stack. Frontier · Aug 2025 · 90 citations Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning More stable RL for image generators, plus a better yardstick for them. Frontier · Nov 2025 · 75 citations DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research It shows how to use RL where there is no verifiable answer. Frontier · Nov 2025 · 60 citations DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning Self-verification is a path to reasoning where no answer key exists.