Expired60% confidenceBenchmarkExact time
In the inventory management task, the RL policy scored 194.5, slightly below the base-stock baseline's 196.7, but rediscovered the analytically optimal base-stock policy from scratch.
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/15/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 10/13/2026(expired)
Sources
Related Claims
UnverifiedIn an inventory management task, the cumulative reward of 194.5 learned by RL is lower than the base-stock baseline of 196.7.77% similarUnverifiedThe general-purpose policy model π_{0.5} has a grasping success rate of 0% in real deployment scenarios without adaptation.63% similarUnverifiedThe key modification in RLCD is replacing the 0/1 reward with a proper scoring rule.60% similarUnverifiedOn performance model code construction tasks, the average pass rate of all model configurations other than GPT-5.6 Sol was below 15%, with extremely large fluctuations across multiple runs.58% similarUnverifiedWhen the budget was concentrated on the reflection/control slot, the success rate recovered to 0.761 even with only half of the split budget.58% similar
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