Unverified50% confidenceFactExact time
Supervised fine-tuning (SFT) trains a model using 'prompt–response' example pairs, transforming it from outputting gibberish to being capable of meaningful question answering.
1
Sources
50%
Confidence
Long-term
Relevance
8/31/2026
First Seen
Sources
Related Entities
Related Claims
Unverified测试时训练(TTT)在推理阶段利用当前输入的自监督信号对部分参数进行短暂局部梯度更新再执行预测73% similarUnverifiedSome code-specific models actually execute the generated code during training and use the test results as a reward signal for reinforcement learning (execution feedback).69% similarUnverified成熟的自动化测试团队更倾向于使用pytest+requests或RestAssured等代码化框架而非Newman66% similarUnverified在提示中包含明确验收标准的输出约束设计,本质上类似强化学习中奖励函数的作用66% similarUnverifiedNormalization parameters must be calculated only from the training set and then applied to the validation and test sets to avoid data leakage.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/830473API
curl https://kongchang.com/api/v1/knowledge/claims/830473MCP
get_claim(id=830473)