Unverified50% confidenceSolutionExact time
Using an unbiased leave-one-out estimator can recover most of RLCD's performance gap.
1
Sources
50%
Confidence
Long-term
Relevance
10/5/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedRLCD's reward normalization mechanism amplifies the noisy score function term by 3.6 to 15 times, which is the main reason for its performance gap.67% similarUnverifiedData normalization makes the loss surface closer to spherical, allowing optimizers to find the optimal solution more efficiently and avoiding oscillation and slow convergence caused by differences in feature scale.67% similarUnverifiedRAPID employs an inverse proposal correction technique to ensure the loss function and gradient estimator are unbiased under conditional expectation66% similarUnverifiedThe improvements of RLDS on HotpotQA and DeepResearch fall within the noise range, consistent with the diagnostic prediction that these two tasks have limited recoverable information.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/975542API
curl https://kongchang.com/api/v1/knowledge/claims/975542MCP
get_claim(id=975542)