ML//Inference//extended thinking//overthinking

Documented empirically: forcing long extended thinking on simple problems makes the model **worse**, not better.

Documented empirically: forcing long extended thinking on simple problems makes the model worse, not better.

On simple questions where the direct answer has high probability from the start, thinking introduces:

More opportunities for self-consistency bias — the model prefers branches consistent with its own pretraining biases, not necessarily the most correct ones.

The model can "convince itself" to change a correct answer — reasoning generates context that activates irrelevant complexity.

Drift toward regions of latent space associated with unnecessary complexity.

Papers show o1 performs worse than GPT-4 on simple common-sense questions precisely because of this — the thinking generates context that triggers a distributional shift to the wrong region.

Token budgets (like Anthropic's) are partially a solution — not just cost control, but preventing the model from thinking too much on problems that don't need it.

The deeper problem: the model has no reliable metacognition. It can't assess "do I need to think more?" — it just generates tokens until told to stop.