Introducing Mistral OCR 3
Introducing Mistral OCR 3
번역 대기 중 · 원문 표시 · enAI NEWS JOURNAL
전체 AI 소식
출처, 주제, 시간으로 전체 뉴스에서 관심 소식을 찾아보세요.
Introducing: Devstral 2 and Mistral Vibe CLI.
Introducing: Devstral 2 and Mistral Vibe CLI.
번역 대기 중 · 원문 표시 · enIntroducing Mistral 3
Introducing Mistral 3
번역 대기 중 · 원문 표시 · enRL without TD learning
In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer. Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalability challenges), and scales well to long-horizon tasks. We can do Reinforcement Learning (RL) based on divide and conquer, instead of temporal difference (TD) learning. Problem setting: off-policy RL Our problem setting is off-policy RL. Let’s briefly review what this means. There are two classes of algorithms in RL: on-policy RL and off-policy RL. On-policy RL means we can only use fresh data collected by the current policy. In other words, we have to throw away old data each time we update the policy. Algorithms like PPO and GRPO (and policy gradient meth
번역 대기 중 · 원문 표시 · enWhat exactly does word2vec learn?
What exactly does word2vec learn, and how? Answering this question amounts to understanding representation learning in a minimal yet interesting language modeling task. Despite the fact that word2vec is a well-known precursor to modern language models, for many years, researchers lacked a quantitative and predictive theory describing its learning process. In our new paper, we finally provide such a theory. We prove that there are realistic, practical regimes in which the learning problem reduces to unweighted least-squares matrix factorization. We solve the gradient flow dynamics in closed form; the final learned representations are simply given by PCA. Learning dynamics of word2vec. When trained from small initialization, word2vec learns in discrete, sequential steps. Left: rank-increment
번역 대기 중 · 원문 표시 · en