The core idea: instead of giving every query the same "thinking time," classify queries as HIGH or LOW complexity and allocate thinking tokens accordingly. Complex reasoning gets 70-90% of tokens, simple queries get 20-40%.

I also implemented steering vectors derived from Pivotal Token Search (originally from Microsoft's Phi-4 paper) that guide the model's reasoning patterns during generation. These vectors encourage behaviors like numerical accuracy, self-correction, and thorough exploration.

Results on DeepSeek-R1-Distill-Qwen-1.5B:

- GPQA-Diamond: 31.06% vs 21.72% baseline (+43% relative improvement)

- MMLU-Pro: 26.38% vs 25.58% baseline

- Uses fewer tokens than baseline approaches

Works with any local reasoning model - DeepSeek, Qwen, custom fine-tuned models. No API dependencies.

The technique builds on two things I developed: an adaptive classification framework that can learn new complexity categories without retraining, and an open source implementation of Pivotal Token Search.

Technical paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5253327

Code and examples: https://github.com/codelion/optillm/tree/main/optillm/autoth...

PTS implementation: https://github.com/codelion/pts

I'm curious about your thoughts on adaptive resource allocation for AI reasoning. Have you tried similar approaches with your local models?