Unverified50% confidenceFactExact time
Experience replay in continual learning is usually implemented in two ways: rehearsal (maintaining a buffer of old-task samples and mixing old samples in when training new tasks) and pseudo-rehearsal (using generative models to synthesize old data).
1
Sources
50%
Confidence
Long-term
Relevance
10/28/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedUpdating parametric memory requires fine-tuning or continual pre-training, both of which require additional training processes and computational resources.77% similarUnverifiedExperiments usually adopt a multi-round active learning loop: train the model, select samples, query labels, and retrain.76% similarUnverifiedSpecialized training for long-horizon tasks typically requires constructing supervised data containing complete task trajectories, rather than simple single-turn question-answer pairs.74% similarUnverifiedTrigger-based retraining (based on performance degradation monitoring and data drift detection) can replace scheduled full retraining, directing computational resources precisely where they are truly needed74% similarUnverifiedContinual training is a method of continuously training on newly added data to keep the model's knowledge up to date.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1109279API
curl https://kongchang.com/api/v1/knowledge/claims/1109279MCP
get_claim(id=1109279)