Unverified50% confidenceSolutionExact time
PRISMA uses an adaptive doubling procedure to adjust the regularization strength λ online without knowing ρ*, avoiding the requirement of knowing the instance difficulty in advance and incurring only logarithmic additional overhead.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedPRISMA introduces an instance-dependent difficulty measure ρ* (taking values between 1 and d), which characterizes the intrinsic difficulty of designing a shared sampling distribution; a smaller ρ* indicates stronger commonality among policies and high efficiency of shared sampling.71% similarUnverifiedThe newly launched Ultra mode provides a reasoning budget far exceeding normal limits, but consumes token quota more quickly.67% similarUnverifiedMoE has a clear efficiency advantage under large-batch inference, but in single-request, low-latency scenarios the additional overhead of routing scheduling may offset the gains from sparse activation.67% similarUnverifiedThis method still needs to be validated and extended in terms of high-dimensional state spaces, non-time-homogeneous processes, and computational efficiency.66% similarUnverifiedManually rewriting prompts is neither reproducible nor scalable, and DSPy supports systematic optimization at both the few-shot example and instruction levels65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1042071API
curl https://kongchang.com/api/v1/knowledge/claims/1042071MCP
get_claim(id=1042071)