We study the problem of learning an n-variables k-CNF formula Φ from its i.i.d. uniform random solutions, which is equivalent to learning a Boolean Markov random field (MRF) with k-wise hard constraints. Revisiting Valiant’s algorithm (Commun. ACM’84), we show that it can exactly learn (1) k-CNFs with bounded clause intersection size under Lovász local lemma type conditions, from O(logn) samples; and (2) random k-CNFs near the satisfiability threshold, from O(nexp(−√k)) samples. These results significantly improve the previous O(nk) sample complexity. We further establish new information-theoretic lower bounds on sample complexity for both exact and approximate learning from uniform random solutions.