A single experiment on a four-billion-parameter Qwen model reopens a central question for anyone running LLMs locally: how much do hesitation tokens affect accuracy, and how much of the damage can be recovered without fine-tuning? The test starts from Meta's paper (arXiv 2606.00206) and applies a logit bias to tokens indicating second thoughts ('wait', 'maybe', 'perhaps', 'however', 'alternatively', 'wrong', 'error' and many others). On 50 random MATH-500 questions, accuracy improves across all GGUF quantization levels tested.

The numbers should be read with caution. BF16 goes from 74% to 84% with a 19.4% reduction in reasoning tokens. Q8_0 goes from 76% to 80%, Q4_K_M from 60% to 66%, Q3_K_M from 52% to 66%, and Q2_K from 12% to 24%. It is not a uniform curve: the largest relative gain is on Q2_K, the most aggressive quantization level, whose result doubles. Q3_K_M is interesting because with the penalty it surpasses Q4_K_M without the penalty, at least on this sample.

Here is the structural point. Penalizing overthinking does not touch model weights, does not require GPUs with large amounts of VRAM, and introduces no cloud dependencies. It is an inference-time intervention that acts on the output token distribution. For the self-hosted scenario — where llama.cpp and GGUF formats are a de facto standard for local inference — this kind of lever can reduce computational cost without changing hardware. Fewer reasoning tokens generated mean fewer compute steps and potentially lower latency, a relevant factor when the model runs on machines with shared memory or consumer GPUs. From a TCO perspective, reducing the number of tokens produced without changing model or hardware has direct value.

The downside is equally clear. The test uses a single model, 50 questions, and a token list decided upfront. It is not possible to claim statistical significance from one test on one model, and the result may not replicate on other benchmarks, model families, or tasks. A fixed logit bias on hesitation markers can also have side effects: penalizing 'but', 'or' or 'however' may help on a math task but reduce the ability to produce conditional reasoning in linguistic or creative contexts. The choice of token IDs depends on the tokenizer and requires attention: the values need to be checked against the vocabulary of the GGUF model.

For those evaluating on-premise deployments, the signal is twofold. On one side, the results show that the choice of quantization level is not just a compromise between numerical precision and memory savings: the behavior of reasoning tokens also plays a role. On the other, interventions such as logit bias are cheap and reproducible within llama.cpp, but they need to be tested on your own domain before being considered part of the pipeline. The am17an experiment is not a recommendation; it is an invitation to measure: it worked on a small sample, but real-world scale needs more solid data.