GoKawiilEssay reframes AI 'reasoning' as pathfinding, not stepwise convergence
A blog post argues that chain-of-thought reasoning in language models is better understood as pathfinding through token space rather than a process that steadily converges toward a correct answer. The author notes that reinforcement learning training increases both the length and accuracy of reasoning chains, yet forcibly extending a single trained chain at inference time only helps briefly before accuracy plateaus or worsens. The piece proposes that RL instead increases the likelihood of finding some successful path to an answer, without guaranteeing any individual chain improves as it gets longer.