SCLATE: A Substrate for Continual-Learning Agent Training and Evaluation
Continual-learning agents are systems of models, harnesses, and memory operating over long multi-session horizons. Evaluating and training them requires interleaving tasks with agent-side events such as session stop and start, crons, and memory consolidation. Yet existing benchmarks and training frameworks schedule only the benchmark’s own events, leaving each benchmark and agent pair to build a custom scheduling...
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Qué ocurrió
Continual-learning agents are systems of models, harnesses, and memory operating over long multi-session horizons. Evaluating and training them requires interleaving tasks with agent-side events such as session stop and start, crons, and memory consolidation. Yet existing benchmarks and training frameworks schedule only the benchmark’s own events, leaving each benchmark and agent pair to build a custom scheduling...
Por que importa
The development may change operating conditions or market expectations around AI. Further confirmation and measurable outcomes matter.
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1 articulos · 1 informe original · 1 independientes
- Apple Machine Learning ResearchFuente primaria · Respalda · EN · 100%SCLATE: A Substrate for Continual-Learning Agent Training and Evaluation ↗
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- SCLATE: A Substrate for Continual-Learning Agent Training and Evaluation Observado
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