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.
GoKawiil's interpretation of the reporting above, not reported fact.
This framing could help explain puzzling behaviors in reasoning models, such as 'overthinking' where longer outputs sometimes produce worse answers, as well as previously noted issues like unfaithful chains of thought and reward hacking, according to the author. If correct, it suggests that optimizing models for longer reasoning traces is not equivalent to optimizing for better step-by-step logic, which could influence how researchers design and evaluate RL training for reasoning models. The author frames this as a conceptual clarification rather than new experimental evidence.
- Forcing a trained model's single chain of thought to run longer helps briefly, then plateaus or hurts accuracy.
- The author proposes viewing reasoning as pathfinding through token space rather than linear convergence toward an answer.
- RL training may increase the chance of finding *some* correct path, not necessarily improve each individual chain's logic.
Source: alexwang.ai — Alex Wang, 2026-11-10
Published there as: “What objective is reasoning solving?”
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