arXiv 2606.13603 investigates a striking phenomenon in reasoning models: 55% of the CoT (Chain-of-Thought) steps in a typical reasoning chain have zero effect on the final answer probability. The "useless thinking" is a significant source of inefficiency, and the paper proposes methods to identify and eliminate it.
The "useless thinking" measurement: the authors use a "counterfactual" analysis — for each CoT step, they remove the step and measure the change in the final answer probability. If the probability doesn't change, the step is "useless." Across a set of 1,000 reasoning chains, 55% of steps are useless on average.
The "where are the useless steps" analysis: useless steps are concentrated in the "exploration" phase of reasoning (the model tries multiple approaches, most of which are abandoned) and the "verification" phase (the model re-checks its work, often redundantly). The "execution" phase (the model commits to an approach and works through it) has very few useless steps.
The fix: a "step pruning" method that removes useless steps during inference. The pruning is done in real time, with a small classifier predicting whether each step is useless. The pruned reasoning chains are 50% shorter, with no quality loss. The latency is also reduced, since fewer tokens need to be generated.
The bigger takeaway: "reasoning efficiency" is a real engineering discipline. The "more thinking is better" assumption is breaking, and the "prune the useless thinking" approach is significantly more efficient. For the industry, this means reasoning models will adopt step-pruning techniques, and the next round of reasoning efficiency improvements will come from this direction.