🎲 Apprendre à un réseau à écrire avec seulement une récompense — il a atteint 99,9 % grammatical sans apprendre une seule règle 🇫🇷

The article explores the possibility of teaching a neural network to write using only a reward signal, without pretraining or linguistic knowledge. Two experiments were conducted: the first involved copying a fixed phrase, successfully achieved in 1,639 episodes due to the problem's decomposition into simpler sub-problems; the second aimed at learning grammar judged by a hand-written parser, achieving 99.9% grammaticality by exploiting a degenerate sub-language where grammatical agreement was trivial. The study challenges the notion of "sparse reward" and suggests replacing it with an analysis of reward variance distribution. The author finds that the failure of pure reinforcement learning (RL) is not due to reward sparsity, but rather the low probability of encountering successful outcomes. A proposed solution involves annealing the entropy coefficient to enhance performance. The article concludes that a high score on a hand-crafted verifier does not guarantee rule learning, as the network may simply find a corner of the output space where constraints are vacuous.

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