Unverified50% confidenceFactExact time
ClueFlow's attribution mechanism determines which decisions should be supervised in the SFT stage, learning only from state changes consistent with the gold standard and their original interaction contexts.
1
Sources
50%
Confidence
Long-term
Relevance
10/20/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedClueFlow pushes credit assignment down from the turn granularity to the state-change granularity, enabling the model to learn which type of evidence-acquisition action leads to which type of state evolution.71% similarUnverifiedSFT cold start refers to first using supervised fine-tuning to give the model basic target behavior patterns before reinforcement learning, in order to avoid the extremely sparse reward signal problem when RL explores from scratch.66% similarVerifiedDomain fine-tuning typically first injects domain knowledge through supervised fine-tuning (SFT), then aligns with user preferences through reinforcement learning from human feedback (RLHF).65% similarUnverifiedKITE uses key layers as the entry point for autoregressive latent inference, from which the receiver unfolds subsequent reasoning.63% similarVerifiedSupervised Fine-Tuning (SFT) trains the model using 'prompt-response' example pairs, teaching the model to respond in a conversational format, thereby making outputs more coherent.62% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1073981API
curl https://kongchang.com/api/v1/knowledge/claims/1073981MCP
get_claim(id=1073981)