What objective is reasoning solving?
Summary
The article reframes reasoning in language models as pathfinding in token space, introducing the idea of a verifier and a success probability p_x. It covers how best-of-k sampling and training to generate better samples (including REINFORCE) inflate the chance of landing in the correct answer, and discusses amortization, generalization, and concerns like chains of thought fidelity and reward hacking.