Logic Distillation: Learning from Code Function by Function for Planning and Decision-making (2407.19405v1)

Abstract: LLMs have garnered increasing attention owing to their powerful logical reasoning capabilities. Generally, larger LLMs (L-LLMs) that require paid interfaces exhibit significantly superior performance compared to smaller LLMs (S-LLMs) that can be deployed on a variety of devices. Knowledge distillation (KD) aims to empower S-LLMs with the capabilities of L-LLMs, while S-LLMs merely mimic the outputs of L-LLMs, failing to get the powerful logical reasoning capabilities. Consequently, S-LLMs are helpless when it comes to planning and decision-making tasks that require logical reasoning capabilities. To tackle the identified challenges, we propose a novel framework called Logic Distillation (LD). Initially, LD employs L-LLMs to instantiate complex instructions into discrete functions and illustrates their usage to establish a function base. Subsequently, based on the function base, LD fine-tunes S-LLMs to learn the logic employed by L-LLMs in planning and decision-making. During testing, LD utilizes a retriever to identify the top-$K$ relevant functions based on instructions and current states, which will be selected and invoked by S-LLMs. Ultimately, S-LLMs yield planning and decision-making outcomes, function by function. Relevant experiments demonstrate that with the assistance of LD, S-LLMs can achieve outstanding results in planning and decision-making tasks, comparable to, or even surpassing, those of L-LLMs.

Insightful Overview of "Logic Distillation: Learning from Code Function by Function for Planning and Decision-making"

The paper "Logic Distillation: Learning from Code Function by Function for Planning and Decision-making" addresses the limitations often encountered in deploying Smaller-LLMs (S-LLMs) for tasks requiring complex logical reasoning capabilities. Larger-LLMs (L-LLMs) such as GPT-3.5 and GLM-4, despite their advanced capabilities, demand substantial computational resources that make them impractical for widespread deployment. Conversely, S-LLMs are more flexible in deployment but lack the superior performance levels of their larger counterparts. Existing approaches such as Knowledge Distillation (KD) largely focus on having S-LLMs mimic the output of L-LLMs, failing to endow them with inherent logical reasoning capabilities.

Key Contributions and Methodology

The authors propose a novel framework termed "Logic Distillation" (LD), which aims to equip S-LLMs with planning and decision-making capabilities on par with L-LLMs. This involves breaking down the reasoning logic of L-LLMs into discrete functions. The process unfolds in distinct phases:

1. Decomposition into Functions: L-LLMs leverage their comprehension ability to deconstruct complex instructions into a series of functions, forming a comprehensive function base. These functions encapsulate planning and decision-making logic commonly utilized by L-LLMs.
2. Adaptation for S-LLMs: S-LLMs are fine-tuned using this function base, enabling them to understand the underlying logic and apply it effectively in practical scenarios. During testing, a retriever is implemented to identify the top-$K$ relevant functions, which S-LLMs then select and invoke as they execute planning and decision-making tasks.

The framework primarily centers on shifting from a generation-focused to a selection-focused paradigm, hypothesizing this transformation will yield better stability and effectiveness in S-LLM outputs.

Experimental Evaluation and Results

The paper employs a pursuit game to evaluate the efficacy of LD. Through these experiments, results demonstrate that S-LLMs utilizing LD can achieve performance levels in planning and decision-making tasks that are comparable to, or even surpass, those of L-LLMs. Numerical results reflect a 100% success rate in task completion for S-LLMs with LD, compared to slightly lower outcomes in those employing KD or merely the unaided LLMs. Additionally, the LD approach enabled S-LLMs to handle emergencies more effectively, reaffirming the robust generalizability conferred by the method.

Implications and Future Directions

From a theoretical standpoint, the proposed LD approach provides a structural shift in how S-LLMs can be empowered to exhibit logical reasoning and decision-making capabilities without simply mimicking the often complex outputs of L-LLMs. Practically, LD represents a significant advancement in making efficient, cost-free LLMs more viable for real-world scenarios that demand intricate decision-making processes. The framework not only enhances the immediate deployment capabilities of AI models but also opens avenues for future research into more context-aware AI systems.

In conclusion, while current results are promising, future research will likely explore refining the LD approach to expand its application across varying domains of AI. Such advancements could involve more nuanced retrieval methods, richer function decomposition strategies, or broader simulation environments to further enhance the capability of S-LLMs. This paper provides a pivotal step towards more autonomous and intelligent systems capable of nuanced understanding and decision-making.