Unverified50% confidenceFactExact time
Sparse rewards only provide signals when a task succeeds or fails, which leads to extremely low exploration efficiency in long-horizon, multi-stage tasks.
1
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50%
Confidence
Long-term
Relevance
10/12/2026
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Related Claims
UnverifiedIn reward function design, sparse rewards can easily cause training to stagnate, and introducing dense reward signals such as distance reduction can accelerate convergence.79% similarUnverifiedRewards 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.78% similarUnverifiedThe quality of reward signals in RLVR depends on the task difficulty distribution: tasks that are too easy lead to reward saturation and vanishing gradients, while tasks that are too hard lead to sparse rewards, both of which cause training to stall.77% similarUnverifiedIn multi-step tasks, tool-call failures accumulate, and a high single-step failure rate causes the overall success rate to decline exponentially.77% similarUnverifiedIn tasks lacking repetitive patterns, such as free-form creative writing and open-ended question answering, the hit rate is low, and the speedup benefit drops significantly, sometimes even incurring slight overhead due to verification costs.76% similar
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