Unverified50% confidenceFactExact time
In the early stages of training, models often need to process large numbers of samples that contribute little to final performance, constituting significant resource waste.
1
Sources
50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedExact unlearning achieves complete deletion by retraining the model after removing the target data, which is extremely costly for large models with billions of parameters, as a single complete training run may consume millions of dollars in computing resources.75% similarUnverifiedAfter large models entered the post-pretraining era, the capability gains from simply stacking parameters have become increasingly diminishing, and vendors have turned to the post-training phase in search of breakthroughs.75% similarUnverifiedTraditional large model training relies heavily on manually annotated data and external corpus collection, which are typically the bottlenecks constraining iteration speed.74% similarUnverifiedTraditional draft models are trained on narrow distributions targeting a single model, causing acceptance rates to drop sharply when workloads change, and requiring the entire pre-training process to be redone each time a target model is replaced.74% similarUnverifiedSimply stacking parameters without sufficient training data results in a model that is large but not capable; a leap in parameters must be accompanied by a proportionally expanded training corpus and computing power to realize capability gains.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/906696API
curl https://kongchang.com/api/v1/knowledge/claims/906696MCP
get_claim(id=906696)