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Alignment generally refers to making a model's behavior, judgments, and outputs conform to human intent and values; common methods include reinforcement learning from human feedback, preference optimization, and behavioral specification constraints.
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11/3/2026
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VerifiedThe goal of alignment research is to ensure that a model's objectives and behavior conform to human intent, avoiding misleading outputs or value misalignment.75% similarUnverifiedWhether a model can follow human instructions (instruction following) is one of the foundational metrics of controllability74% similarUnverifiedMetrics define behavior—whatever you reward, the model learns to do, even if it deviates from the truly desired outcome.74% similarVerifiedLanguage model alignment techniques include Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), aimed at making model behavior conform to human intentions and values73% similarUnverifiedSelf-reflection is achieved by having the model evaluate the quality of its own output and iteratively improve it; the Reflexion framework demonstrates that an agent can learn from failed attempts and adjust its strategy in the next iteration.73% similar
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