Unverified50% confidenceFactExact time
FD-VAD employs a last-chunk training objective specifically designed for streaming inference.
1
Sources
50%
Confidence
Long-term
Relevance
9/30/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe planner was trained via supervised fine-tuning (SFT) on outcome-selection trajectories from 140 training questions and 28 development questions.67% similarUnverifiedA typical post-training pipeline is: pre-training → supervised fine-tuning (SFT) → preference optimization (RLHF / DPO / GRPO).67% similarUnverifiedThe LLM training and inference pipeline includes stages such as tokenization, embedding layers, self-attention mechanisms, feed-forward networks, and residual connections.66% similarUnverifiedThe framework is highly dependent on the review quality of frontier LLMs, and biases inherent in the LLMs may be propagated into the final training data.66% similarUnverified"Post-training" is the alignment and capability enhancement phase following pre-training, and typically includes SFT, RLHF, and reinforcement learning based on process reward models (such as GRPO and DAPO).64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/961125API
curl https://kongchang.com/api/v1/knowledge/claims/961125MCP
get_claim(id=961125)