AI / SIGNALS / KOREA
최신 AI 모델·에이전트 소식을 한국어로 정리하고, 실제 선택에 필요한 맥락까지 함께 전합니다.
인지 범주형 트랜스포머(Cognitive Categorical Transformer, CCT)는 대규모 언어 모델(LLM)인 GPT-2 Small의 백본에 범주론적 수학적 전제를 기반으로 한 지식 요소를 추가하여 언어 모델링 성능을 향상시킨다. CCT는 WikiText-103 데이터셋에서 21.27의 검증 퍼플렉스성(perplexity)를 달성하였으며, 동일한 조건하에 훈련된 GPT-2 Small의 baseline보다 2.92 PPL 감소가 기록되었다.
시간 차이 학습은 오프-정책 샘플링에서 instable할 수 있습니다. TDC는 오프-정책 TD를 안정화시키기 위해 보조 공분산 정정을 사용하고, TDRC는 이 정정을 단일 시간 규모 재귀로 정규화합니다. 이 연구에서는 선형 예측 설정에서 행동 기반 보조 공분산 기하학을 대체하는 것을 연구합니다. 이 기하학은 가치 함수 근사 공간의 특성 공간 동력학을 이해하는 표준 지역 모델입니다.
gradient 시간 차이 방법은 선형 함수 근사에서 안정적인 오프- полити 예측을 제공하지만, 그 실제 성능은輔助 변수의 기하학적 구조에 의해 강하게 영향을 받는다. 기존의 Mirror-Prox TD 방법은 일반적으로 특성 공분산 기하학을 사용한다. 반면, 하이브리드 TD 방법은 행동-정책 전이 정보가 더 정보가 풍부한 업데이트기하학을 제공할 수 있다고 제안한다. 이 논문은 행동-정책
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