NVIDIA COMPASS
NVIDIA COMPASS is a framework and trained model for cross-embodiment robot navigation. Its name expands to Cross-Embodiment Mobility Policy via Residual RL and Skill Synthesis.
Reinforcement Learning
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Showing 61-118 of 118 articles
NVIDIA Isaac Lab
NVIDIA Isaac Lab is an open-source, GPU-accelerated framework for robot learning that trains robot control policies at scale by running thousands of physics simulations in parallel on a single graphics card
Nash equilibrium
A Nash equilibrium is a combination of strategies, one for each player in a game, such that no player can raise their own payoff by changing strategy alone while everyone else keeps theirs unchanged.
Online learning
Online learning is a machine learning paradigm in which a model receives data sequentially, one example or one mini-batch at a time, and updates its parameters immediately after each observation rather than…
OpenAI Baselines
OpenAI Baselines is a collection of open-source, high-quality reference implementations of reinforcement learning (RL) algorithms released by OpenAI.
OpenAI Five
OpenAI Five was a reinforcement learning system developed by OpenAI to play the competitive multiplayer video game Dota 2 at a professional level.
Pieter Abbeel
Pieter Abbeel (born 1977) is a Belgian-American computer scientist and a professor of electrical engineering and computer sciences at the University of California, Berkeley, where he directs the Berkeley Robot…
Pluribus (poker AI)
Pluribus is an artificial intelligence program that defeated elite human professionals at six-player no-limit Texas hold'em, the most popular form of poker played by people.
Policy
In reinforcement learning (RL), a policy is the function that maps an agent's observed state to the action it takes, defining the agent's complete strategy of behavior.
Policy gradient methods
Policy gradient methods are a family of reinforcement learning algorithms that directly parameterise the agent's policy and optimise it by stochastic gradient ascent on the expected return.
Process reward model (PRM)
A process reward model (PRM), also called a process-supervised reward model or step-level verifier, is a learned scoring model that evaluates the correctness or quality of each intermediate step in a large…
Proximal Policy Optimization (PPO)
Proximal Policy Optimization (PPO) is an on-policy policy gradient reinforcement learning algorithm that stabilizes training by clipping the policy update so the new policy stays close ("proximal") to the old…
Q-Function
The Q-function, also called the action-value function or state-action value function and written $$Q(s, a)$$, is the function in reinforcement learning (RL) that returns the expected cumulative discounted…
Q-Learning
Q-learning is a model-free, off-policy reinforcement learning algorithm that learns the value of taking a given action in a given state by iteratively updating an action-value function Q(s, a), the expected…
Quiet-STaR
Quiet-STaR is a self-supervised training method that teaches a large language model to generate short, token-level internal "thoughts," or rationales, that help it predict the text that follows
RAGEN-2
RAGEN-2 is a 2026 research paper and public code extension for diagnosing and mitigating reasoning collapse during reinforcement learning of multi-turn large language model agents.
RLAIF
Reinforcement Learning from AI Feedback (RLAIF) is a family of alignment techniques for large language models in which the preference labels used to fine-tune a model are produced by another AI system
RLOO (REINFORCE Leave-One-Out)
RLOO (REINFORCE Leave-One-Out) is an online reinforcement learning algorithm for aligning large language models with reward signals such as those derived from human preferences.
RLVR
Reinforcement Learning with Verifiable Rewards (RLVR) is a post-training paradigm for large language models in which the reward signal comes from a deterministic
Random Policy
A random policy is a reinforcement learning policy that chooses actions from a fixed probability distribution, most commonly the uniform distribution, independent of the current state and of any learned value…
ReST / ReST-EM (Reinforced Self-Training)
ReST (Reinforced Self-Training) is a family of self-training algorithms for large language models that improve a model by fine-tuning it on its own filtered outputs instead of on additional human-written data.
Recursive reward modeling
Recursive reward modeling (RRM) is a proposed approach to the scalable oversight problem in AI alignment
Reinforcement learning
Reinforcement learning (RL) is a branch of machine learning in which an agent learns to make decisions by taking actions in an environment to maximize a cumulative reward signal, discovering good behavior…
Replay Buffer
A replay buffer (also called an experience replay buffer or replay memory) is a fixed-size memory that stores an off-policy reinforcement learning agent's past transitions, each a tuple of state, action…
Return (Reinforcement Learning)
In reinforcement learning, the return (commonly denoted $$G_t$$) is the total cumulative reward an agent receives from time step $$t$$ onward, usually with future rewards discounted by a factor $$\gamma$$.
Reward
In reinforcement learning (RL), a reward is a scalar feedback signal that an environment sends to an agent after each action, quantifying how desirable that action's outcome was
Reward Model
A reward model (RM) is a model trained to score the outputs of another AI system, producing a scalar estimate of how good a candidate response is according to some standard, most often human preference.
Reward hacking
Reward hacking (also called specification gaming) is a failure mode in artificial intelligence in which a system maximizes its given objective or reward signal through unintended shortcuts, exploits, or…
RewardBench
RewardBench is a benchmark and public leaderboard for evaluating reward models, the scoring functions that sit at the center of reinforcement learning from human feedback (RLHF).
Richard S. Sutton
Richard Stuart Sutton (born 1957 or 1958) is a Canadian-American computer scientist regarded as one of the founders of modern computational reinforcement learning, and a co-recipient, with Andrew G. Barto
Robot learning
Robot learning studies how robots acquire or improve behavior from data and experience.
Robotics Models
Robotics models are machine learning systems that give robots the ability to perceive their surroundings, plan actions, and execute motor control.
SARSA (State-Action-Reward-State-Action)
SARSA (State-Action-Reward-State-Action) is an on-policy temporal-difference (TD) control algorithm that learns the action-value function Q^pi(s, a) of the policy an agent is actually following
SONIC (Humanoid Control)
SONIC, short for Supersizing mOtion tracking for Natural humanoId Control, is a physics-based whole-body motion tracking and control system developed by Nvidia.
SPADE (Self-Play in Adaptive Synthetic Executable Environments)
SPADE, short for Self-Play in Adaptive Synthetic Executable Environments, is a reinforcement-learning framework in which one language model alternates between designing executable training environments and…
STaR (Self-Taught Reasoner)
STaR (Self-Taught Reasoner) is a self-training method that teaches a large language model to reason by having it generate its own chain-of-thought rationales, keeping only the rationales that lead to correct…
Selective Language Modeling (Rho-1)
Selective Language Modeling (SLM) is a pretraining objective for language models that applies the training loss to only a chosen subset of tokens rather than to every token in the corpus.
Sergey Levine
Sergey Levine is an American computer scientist, associate professor of electrical engineering and computer sciences at the University of California, Berkeley, and a co-founder of Physical Intelligence
Sim-to-real transfer
Sim-to-real transfer (also written as sim2real) is the process of training a policy, controller, or perception model inside a physics simulator and deploying it on a physical robot in the real world without…
Simulation (in AI and robotics)
Simulation in artificial intelligence and robotics is the use of computational physics, rendering, and procedural environments to recreate a synthetic version of a physical or virtual world inside which AI…
Soft Actor-Critic
Soft Actor-Critic (SAC) is an off-policy, maximum-entropy deep reinforcement learning algorithm that trains a stochastic actor-critic to maximize expected reward plus the entropy of its own policy, so the…
Sparse upcycling
Sparse upcycling is a technique for building a sparsely activated mixture of experts (MoE) model by initializing it from an already trained dense Transformer checkpoint
Specification gaming
Specification gaming is the phenomenon in which an optimizer satisfies the literal specification of an objective without producing the outcome that the designer actually wanted.
Spinning Up
Spinning Up in Deep RL is a free, open-source educational resource produced by OpenAI to make deep reinforcement learning (deep RL) easier to learn.
State (Reinforcement Learning)
In reinforcement learning (RL), a state is a complete description of the environment at a particular point in time, containing all the information an agent needs to choose its next action.
State-Action Value Function
The state-action value function, written Q^π(s, a) and also called the action-value function or Q-function, gives the expected discounted return an agent obtains by taking action a in state s and thereafter…
TIES-Merging
TIES-Merging is a training-free model merging method that combines several models fine-tuned from a shared pre-trained checkpoint into one multitask model while explicitly resolving the interference between…
Tabular Q-Learning
Tabular Q-learning is the classic form of Q-learning, a model-free reinforcement learning algorithm that stores the action-value function Q(s, a) explicitly in a lookup table, the Q-table
Target Network
A target network is a separate, slowly updated copy of a neural network used in deep reinforcement learning to compute stable learning targets
Task arithmetic
Task arithmetic is a model-editing technique that steers the behavior of a neural network by adding or subtracting vectors in its weight space.
Temporal-difference learning
Temporal-difference (TD) learning is a class of model-free reinforcement learning methods that learn value-function estimates by bootstrapping: updating each estimate of how good a state is toward a target…
Tim Rocktäschel
Tim Rocktäschel is a German computer scientist known for his work on reinforcement learning, open-ended learning, and language-based AI agents.
Training AI to Paint with Code
Training AI to Paint with Code is an experimental AI art project published by designer and researcher Surya Narreddi in March 2026.
Trajectory (Reinforcement Learning)
A trajectory in reinforcement learning is a sequence of states, actions, and rewards that an agent experiences while interacting with an environment.
Twin Delayed DDPG
Twin Delayed Deep Deterministic Policy Gradient (TD3) is an off-policy actor-critic reinforcement learning algorithm for continuous action spaces, introduced by Scott Fujimoto, Herke van Hoof, and David Meger…
Tülu 3
Tülu 3 is a fully open post-training recipe and a corresponding family of instruction-tuned language models released by the Allen Institute for AI (Ai2) on November 21, 2024.
VAPO (Value-based Augmented PPO)
VAPO (Value-based Augmented Proximal Policy Optimization) is a reinforcement learning framework for training large language models on long chain-of-thought reasoning tasks.
π*0.6 (pi-star-0.6)
π*0.6 (written "Pi-star-0.6") is a vision-language-action robot foundation model developed by Physical Intelligence, a San Francisco robotics startup.