Tyson Fury Confirms Boxing Comeback with Unconventional Training Approach
Tyson Fury is returning to boxing on April 11 against Arslanbek Makhmudov, but he will train without a coach or support staff. Find out why.
#TysonFury, #BoxingComeback, #SelfTraining, #MakhmudovFury, #HeavyweightBoxing
https://newsletter.tf/why-tyson-fury-is-training-alone-for-comeback-fight-april-11/
Tyson Fury is returning to boxing for his fifth comeback fight on April 11. This time, he plans to train without a coach or nutritionist.
#TysonFury, #BoxingComeback, #SelfTraining, #MakhmudovFury, #HeavyweightBoxing
https://newsletter.tf/why-tyson-fury-is-training-alone-for-comeback-fight-april-11/
fly51fly (@fly51fly)
새 논문 'Survival is the Only Reward'는 환경 매개 선택(environment-mediated selection)을 활용한 지속 가능한 자기학습(self-training) 방식을 제안합니다. 보상 신호 대신 생존 기준에 따른 선택으로 에이전트를 선별·학습시켜 샘플 효율성과 안정성을 높이는 접근을 설명하고 관련 실험 결과와 이론적 근거를 제공합니다.
Rohan Paul (@rohanpaul_ai)
한 논문은 LLM이 주로 자기 생성(self-generated) 데이터로 자체 학습(self-training)할 경우 모델의 다양성이 감소하고 진실성에서 벗어나게 된다고 증명한다. LLM은 자체 텍스트만으로 무한히 부트스트랩할 수 없으며 외부의 현실 검증(fresh reality checks) 데이터가 필요하다는 경고를 담고 있다.
This paper proves LLM self training on mostly self generated data makes models lose diversity and drift from truth. LLMs cannot bootstrap forever on their own text, they need fresh reality checks or they collapse. The problem is that many people expect an AI to learn from its
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