AutoThink: Adaptive Resource Allocation Boosts Local LLM Performance by 43%

I recently developed AutoThink, a novel technique designed to enhance the performance of local large language models (LLMs) by adaptively distributing computational resources based on the complexity of user queries. The primary concept behind AutoThink is that not all queries require the same amount of processing time. By classifying queries as either HIGH or LOW complexity, the system can allocate thinking tokens more efficiently. High-complexity queries receive a larger portion of the tokens—typically 70-90%—while low-complexity queries get a smaller share, ranging from 20-40%. To further refine the model's reasoning, I incorporated steering vectors inspired by Pivotal Token Search (PTS), a method described in Microsoft's Phi-4 paper. These vectors influence the model's behavior during the generation process, promoting attributes such as numerical accuracy, self-correction, and comprehensive exploration of problem-solving paths. The results were impressive. When applied to the DeepSeek-R1-Distill-Qwen-1.5B model, AutoThink achieved the following improvements: On the GPQA-Diamond benchmark, which tests general-purpose question answering, the model scored 31.06% compared to the baseline of 21.72%, representing a 43% relative improvement. On the MMLU-Pro benchmark, which assesses multi-lingual reasoning capabilities, the model achieved a marginal improvement, increasing from 25.58% to 26.38%. Additionally, AutoThink used fewer tokens overall compared to baseline approaches, making the system more resource-efficient. One of the key advantages of AutoThink is its versatility. It can be applied to any local reasoning model, including DeepSeek, Qwen, and custom fine-tuned models, without requiring API dependencies. This makes it a valuable tool for developers working with a variety of LLMs. The technique is built on two core components I developed: An Adaptive Classification Framework: This framework can dynamically learn new complexity categories without needing extensive retraining. It allows the system to become more sophisticated over time, adapting to the nuances of different types of queries. An Open-Source Implementation of Pivotal Token Search: This implementation provides the necessary tools to guide the model's reasoning patterns effectively. For those interested in delving deeper into the technical aspects, the full paper is available at this link. You can find the code and example implementations on my GitHub repository here, along with the PTS implementation here. I am eager to hear your thoughts on adaptive resource allocation for AI reasoning. Have you explored similar techniques with your own local models? If so, what were your experiences and outcomes?