Unverified50% confidenceFactExact time
Traditional speculative decoding uses rejection sampling or modified sampling, deciding whether to accept a draft token based on the ratio of the draft and target model probabilities, and resampling from the corrected distribution upon rejection, to guarantee that the output distribution is lossless.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedUncensored fine-tuning typically involves secondary fine-tuning of a base model on datasets that contain no refusal examples, or penalizing unnecessary refusal behavior during the DPO stage.72% similarUnverifiedThe strategy of directly rejecting at the history level while issuing warnings and ignoring at the request level balances runtime robustness with data reliability and is a deliberate trade-off design.69% similarUnverifiedThe official deprecation proposal explicitly lists prompt injection and data exfiltration as security risks associated with sampling.69% similarUnverifiedMTP's verification logic involves dynamic token accept/reject decisions; if the relevant state updates are not correctly reflected in the static graph input tensors, it causes verification results to become invalid and acceptance rates to plummet.68% similarUnverifiedPreference data suffers from selection bias: users willing to make edits are typically only a minority and their editing standards are inconsistent, so filtering, deduplication and consistency checks are usually needed before use.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1038133API
curl https://kongchang.com/api/v1/knowledge/claims/1038133MCP
get_claim(id=1038133)