Unverified50% confidenceFactExact time
Rewards that are too sparse (scored only at the end of an episode) make it very difficult for the agent to obtain effective gradients through random trial and error.
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50%
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Long-term
Relevance
10/26/2026
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Related Claims
UnverifiedSkinner found that when rewards appear at unpredictable intervals (variable-ratio reinforcement), subjects exhibit the most persistent and hardest-to-extinguish behavior.78% similarVerifiedRewards in real-world tasks are often extremely sparse and delayed, causing long-horizon credit assignment problems that make it difficult for agents to determine which action led to the final outcome.77% similarUnverifiedTemporal-difference-based methods struggle to attribute sparse final rewards to early actions, resulting in extremely weak learning signals.77% similarUnverifiedOverly sparse rewards lead to extremely low learning efficiency, while poorly designed rewards can trigger the reward hacking problem.76% similarUnverifiedSparse rewards only provide signals when a task succeeds or fails, which leads to extremely low exploration efficiency in long-horizon, multi-stage tasks.76% similar
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