Direct Behavior Optimization: Unlocking the Potential of Lightweight LLM s (2025)

Abstract: Lightweight Large Language Models (LwLLMs) are reduced-parameter, optimized models designed to run efficiently on consumer-grade hardware, offering significant advantages in resource efficiency, cost-effectiveness, and data privacy. However, these models often struggle with limited inference and reasoning capabilities, which restrict their performance on complex tasks and limit their practical applicability. Moreover, existing prompt optimization methods typically rely on extensive manual effort or the meta-cognitive abilities of state-of-the-art LLMs, making them less effective for LwLLMs.To address these challenges, we introduce DeBoP, a new Direct Behavior Optimization Paradigm, original from the Chain-of-Thought (CoT) prompting technique. Unlike CoT Prompting, DeBoP is an automatic optimization method, which focuses on the optimization directly on the behavior of LwLLMs. In particular, DeBoP transforms the optimization of complex prompts into the optimization of discrete, quantifiable execution sequences using a gradient-free Monte Carlo Tree Search. We evaluate DeBoP on seven challenging tasks where state-of-the-art LLMs excel but LwLLMs generally underperform. Experimental results demonstrate that DeBoP significantly outperforms recent prompt optimization methods on most tasks. In particular, DeBoP-optimized LwLLMs surpass GPT-3.5 on most tasks while reducing computational time by approximately 60% compared to other automatic prompt optimization methods.

## introduction large language models (llms) have revolutionized the field of natural language processing, achieving remarkable performance in diverse tasks such as complex text analysis @xcite , advanced mathematical reasoning @xcite , and creative writing @xcite . however, these advancements are accompanied by significant computational demands, as exemplified by the new llama model, which comprises 405b parameters @xcite . this challenge has driven extensive research efforts to develop efficient alternatives, leading to the emergence of lightweight llms (lwllms) optimized for consumer-grade hardware @xcite . notably, lwllms with 3b-parameter models can operate on mainstream gpus like nvidia®geforce rtx 4060 (8gb vram). moreover, lwllms offer enhanced security and compliance over models accessed via apis @xcite , as they operate entirely within the institution's internal network. this significantly mitigates the risk of sensitive data leakage through public networks @xcite . despite their resource efficiency and enhanced security, lwllms often struggle with advanced inference and multi-step reasoning on complex tasks @xcite . one promising solution to this issue is prompt optimization, which can be broadly classified into manual and automatic techniques. manual methods, while effective, require significant human effort, making them impractical in resource-constrained environments. on the other hand, automatic methods have broader applicability but often rely on the meta-cognitive capabilities @xcite of llms, such as iterative self-reflection @xcite , planning @xcite , and detailed reasoning traces @xcite , which lwllms struggle to handle efficiently. more critically, the hierarchical reasoning structures in these methods can lead to rapid error propagation and accumulation (as illustrated in figure 1 ), which may ultimately to address these challenges, we in this paper introduce debop, a novel direct behavior optimization paradigm, which directly optimizes the behavior of lwllms. inspired by the experimental results in the contributions of this paper can be summarized as follows. 1) we propose an automated behavior optimization method tailored for lwllms, significantly expanding the applicability of llms in resource-and security-sensitive settings. 2) we design an external optimizer based on a gradientfree mcts framework, which effectively quanti-fying prompt design and eliminating the need for advanced meta-cognitive capabilities. 3) we empirically demonstrate the effectiveness of debop across several challenging tasks where state-of-theart llms typically excel @xcite , and only the combination of llama3-8b and de-bop can outperform gpt-3.5 on most tasks while reducing the time consumption of lwllms by approximately 60% compared to other automatic prompt optimization methods. ## methodology as introduced earlier, debop is derived from the cot prompting, which employs a series of demonstrations. in each demonstration, a question is paired with its corresponding solution that represents the behavior of the lwllm. debop focuses on automatically optimizing these solutions and decomposing each solution into key-step plan and their corresponding executions. therefore, debop addresses the generation of key-step plan and executions, the optimization of the solution, and the instructions for lwllms to execute them. accordingly, debop consists of four phases: planning, collecting, mcts and teaching. the corresponding high-level description is given in fig. 2 , and the detailed descriptions of each phase are provided in section 2.2.1 through 2.2.4. ## conclusion in this paper, we introduced debop, a new direct behavior optimization paradigm that au-tonomously enhances the performance of lwllms for a given task. we evaluated debop on seven tasks covering both language comprehension and complex reasoning. the results demonstrate that debop consistently exhibits strong and efficient performance, revealing novel optimization methods for lwllms. furthermore, our in-depth analyses underscore the importance of aligning model capabilities with optimization strategies when applying debop to lwllms. these findings highlight debop's potential as an effective framework for improving both efficiency and accuracy in lwllms, paving the way for future advancements in lwllms optimization.