Unverified50% confidenceBenchmarkExact time
When using a constant entropy reward, the network got stuck in a suboptimal solution in 5 out of 6 runs; after annealing the entropy coefficient from 0.1 to 0.005 over the first 10,000 attempts, the number of times it got stuck dropped to only 1 out of 6.
1
Sources
50%
Confidence
Long-term
Relevance
9/28/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedUnder the uniform transition case, the entropy rate of a Markov chain reaches its maximum value of log N, while under the deterministic transition case the entropy rate is 0.73% similarUnverifiedWhen calculated per byte, Shannon Entropy ranges from 0 to 8, with a value close to 8 indicating that the data is nearly completely random.73% similarPartially VerifiedThe more certain a distribution is, the lower its entropy and the shorter the required encoding; the more random a distribution is (such as a uniform distribution), the higher its entropy.67% similarUnverified任务的输出熵越低（答案越可预测），推测性解码的加速效益越显著；GSM8K接受率从位置0的0.92衰减至0.53，MTBench从0.78急跌至0.1366% similarUnverifiedFinite-sample estimation of entropy rate faces a systematic negative bias problem, where plug-in estimators tend to underestimate the true entropy; this can be compensated using the Miller-Madow correction by adding a (k-1)/(2N) term.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/956502API
curl https://kongchang.com/api/v1/knowledge/claims/956502MCP
get_claim(id=956502)