Patience Is The Key to Large Language Model Reasoning (2411.13082v3)

Abstract: Recent advancements in the field of LLMs, particularly through the Chain of Thought (CoT) approach, have demonstrated significant improvements in solving complex problems. However, existing models either tend to sacrifice detailed reasoning for brevity due to user preferences, or require extensive and expensive training data to learn complicated reasoning ability, limiting their potential in solving complex tasks. To bridge this gap, following the concept of scaling test-time, we propose a simple method by encouraging models to adopt a more patient reasoning style without the need of introducing new knowledge or skills. To employ a preference optimization approach, we generate detailed reasoning processes as positive examples and simple answers as negative examples, thereby training the model to favor thoroughness in its responses. Our results demonstrate a performance increase of up to 2.1% on GSM8k with training just on a lightweight dataset.

An Examination of "Patience Is The Key to LLM Reasoning"

The paper "Patience Is The Key to LLM Reasoning" investigates an intriguing proposition within the field of LLMs: enhancing reasoning capabilities by encouraging more extensive and detailed reasoning processes. The authors present a method that deviates from relying on expansive datasets or re-training models with new knowledge, opting instead to fine-tune existing models to adopt a "patient" reasoning approach.

Methodological Approach

The core of the proposed methodology lies in preference optimization. Existing LLMs often prioritize brevity and speed, aligning with typical user preferences. This effort can unintentionally limit their problem-solving potential, especially in complex domains like mathematical reasoning. The authors advocate for a deliberate shift towards thorough analytical processes by treating detailed reasoning as positive learning instances and concise answers as negative ones.

The execution involves utilizing refined reasoning examples generated from GPT-4o and applying Direct Preference Optimization (DPO) to fine-tune models such that they internalize this more deliberate reasoning. Specifically, the research used the Qwen2-7B-Instruct model as the base and executed DPO training, employing minimal computational resources.

Results and Evaluations

The empirical results substantiate the viability of the approach. The method yields a 6.7% enhancement on the GSM8k benchmark. Comparatively, a slight uptick, though more modest, is noted for the MATH benchmark. While the improved performance is accompanied by an increase in processing time—approximately 3.7 seconds longer on average—the authors view this trade-off as justified by the performance gains. It serves as a holistic demonstration that elevated reasoning quality can be achieved without substantially increasing computational or data-collection demands.

Implications and Future Directions

Practically, this method offers a cost-effective alternative to the commonly adopted route of gathering vast amounts of sophisticated, high-quality training data—a process known for its financial and logistical constraints. Theoretically, it challenges the prevailing notion that expansive datasets are invariably necessary for improving model capabilities in complex tasks. Furthermore, the approach prompts reconsideration of evaluation metrics, balancing latency and accuracy in a more nuanced manner.

The implications extend to reinforcing the adaptability potential inherent in existing LLMs. Fine-tuning for patient reasoning could be explored in other complex tasks, not limited to mathematics, thereby broadening the operational utility of LLMs across various domains.

Conclusions

The research presents a straightforward yet effective methodology that rearranges the strategic focus from data-centric to reasoning-centric enhancements within LLM frameworks. This aligns well with continuous endeavors to optimize AI efficiencies in both development time and cost. Future AI systems could benefit from embracing such inferred adaptive strategies, fostering advancements in AI reasoning capabilities through more deliberate and patient analytical processes. Such strategies open avenues for further exploration into optimizing computational efficacy without compromising on problem-solving sophistication.