RL Post‑Training Turns Latent Skills into Reusable Reasoning Strategies
Fine‑tuning a pretrained transformer with reinforcement learning on a rewrite‑grammar task yields higher‑level compositional procedures that the base model never discovers, even with massive sampling. RL first reinforces primitive reductions, then composes them into sequential and parallel rewrites that are reused across problems, proving it builds genuinely new reasoning strategies.