An outcome often follows many decisions rather than one isolated action. Credit assignment concerns how a learning system distributes the feedback from that outcome among contributing actions or internal components. In reinforcement learning, the temporal form of this problem connects later rewards with earlier decisions.
For a scientific agent, a final experiment score may depend on choosing a method, preparing data and interpreting results. Rewarding only the final response can obscure useful earlier work. Assigning credit is an estimate made by a learning method, not proof that a particular action caused success.
ELI5
Credit assignment means working out which steps helped or hurt a result. An AI needs this information so it can improve the right parts of what it did.
For example, an AI solves a puzzle after ten moves. The last move finishes it, but an earlier move may have opened the way. Learning should recognize useful earlier choices too, not just the final move.
Why are delayed rewards difficult?
When many actions separate a choice from its reward, there are more possible contributors and it is harder to identify which choices mattered.
Is credit assignment only about reinforcement learning?
No. The broader problem also concerns how feedback is attributed to internal parts of a learning system, not only to actions over time.
