Unverified50% confidenceFactExact time
The training pipeline for CRN v2 consists of two steps: supervised fine-tuning (SFT) first, followed by reference-free DPO, using 83,400 error-correction pairs.
1
Sources
50%
Confidence
Long-term
Relevance
9/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedMinor version upgrades are carried out through post-training techniques such as supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), or direct preference optimization (DPO), at a lower cost than full pretraining.68% similarVerified从预训练检查点到可商用产品需经历监督式指令微调（SFT）和基于人类反馈的强化学习（RLHF）两个后训练阶段67% similarUnverified优先经验回放（PER）通过为高TD误差的样本赋予更高的采样概率，将训练资源集中在模型最难处理的经验上65% similarUnverifiedExecutive Order 14110以训练所用浮点运算量（FLOPs）作为触发审查义务的量化阈值，超过10²⁶次FLOPs训练量的模型即需强制报备62% similarUnverifiedA typical post-training pipeline is: pre-training → supervised fine-tuning (SFT) → preference optimization (RLHF / DPO / GRPO).62% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/924663API
curl https://kongchang.com/api/v1/knowledge/claims/924663MCP
get_claim(id=924663)