Unverified50% confidenceFactExact time
By comparing the framework's pass@k curves before and after optimization, one can determine whether the performance gain comes from more stable single-attempt success (an overall shift of the curve) or from expanding the boundary of solvable tasks (the curve rising significantly at high k).
1
Sources
50%
Confidence
Long-term
Relevance
10/21/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe choice of K value in Top-K routing affects the trade-off between computational efficiency and expressive capacity — a smaller K leads to faster inference but may sacrifice expressiveness, and in practice K is typically set to 1 or 2.72% similarUnverifiedWhen nonlinear thresholding metrics (such as exact match rate) are used, model performance appears to change abruptly, whereas when continuous metrics (such as log probability) are used instead, performance improvements are smooth and gradual.71% similarUnverifiedWait-k策略中k值选择在延迟与准确率之间形成权衡,k=0时延迟最低但准确率最差,k趋近句子长度时退化为离线翻译70% similarUnverified推荐的务实路线是分阶段推进:先用阈值方法建立性能基线,待数据积累且不对称代价损失显著后再升级到POMDP68% similarUnverifiedThe essence of routing collapse is a positive feedback loop: an expert gains more tokens due to a slight advantage at initialization, undergoes gradient updates more frequently, adapts its parameters to the data distribution faster, and thereby obtains a higher probability in subsequent routing decisions.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1079318API
curl https://kongchang.com/api/v1/knowledge/claims/1079318MCP
get_claim(id=1079318)