Reward Model
A reward model is a trained model that predicts human preferences between different AI outputs, providing a scalar reward signal. Reward models are central to RLHF and are used to align language models with human values.
Understanding Reward Model
A reward model is a machine learning model trained to predict human preferences, serving as an automated proxy for human evaluation in the training of AI systems through reinforcement learning from human feedback. During RLHF, human annotators rank or compare multiple model outputs for the same prompt, and the reward model learns to assign scalar scores that reflect these human judgments. The language model is then optimized to produce outputs that maximize the reward model's scores, effectively learning to generate responses that humans would prefer. Reward models are critical because they scale the alignment process beyond what direct human evaluation could achieve, enabling optimization over millions of training examples. Challenges include reward hacking, where the language model exploits weaknesses in the reward model to achieve high scores without genuinely improving quality, and distributional shift as the policy model evolves during training. Research in AI safety continues to develop more robust reward modeling approaches to better align AI systems with human values.
Category
Reinforcement Learning
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Deep Reinforcement Learning
Deep reinforcement learning combines deep neural networks with reinforcement learning algorithms to handle complex, high-dimensional environments. It has achieved superhuman performance in games like Go, chess, and Atari.
Exploration vs Exploitation
Exploration vs exploitation is a fundamental dilemma in reinforcement learning between trying new actions to discover better rewards versus leveraging known good actions. Balancing both is key to optimal long-term performance.
Imitation Learning
Imitation learning is a technique where an AI agent learns to perform tasks by observing and mimicking expert demonstrations. It bridges the gap between supervised learning and reinforcement learning.
Inverse Reinforcement Learning
Inverse reinforcement learning infers the reward function that an expert is optimizing by observing their behavior. It enables AI systems to learn goals and preferences from demonstrations.
Markov Decision Process
A Markov Decision Process (MDP) is a mathematical framework for modeling sequential decision-making problems with probabilistic outcomes. MDPs are the formal foundation for reinforcement learning algorithms.
Minimax
Minimax is a decision-making algorithm used in adversarial settings where one player tries to maximize their score while the other minimizes it. It is the classical approach for game-playing AI systems.
Policy
A policy in reinforcement learning is a function that maps states to actions, defining the agent's behavior strategy. The goal of RL is to learn an optimal policy that maximizes cumulative reward.
Q-Learning
Q-learning is a model-free reinforcement learning algorithm that learns the value of actions in states to find an optimal policy. It uses a Q-table or neural network to estimate expected cumulative rewards for each state-action pair.