A Survey on Training-free Alignment of Large Language Models (2025)

Abstract: The alignment of large language models (LLMs) aims to ensure their outputs adhere to human values, ethical standards, and legal norms. Traditional alignment methods often rely on resource-intensive fine-tuning (FT), which may suffer from knowledge degradation and face challenges in scenarios where the model accessibility or computational resources are constrained. In contrast, training-free (TF) alignment techniques—leveraging in-context learning, decoding-time adjustments, and post-generation corrections—offer a promising alternative by enabling alignment without heavily retraining LLMs, making them adaptable to both open-source and closed-source environments. This paper presents the first systematic review of TF alignment methods, categorizing them by stages of **pre-decoding**, **in-decoding**, and **post-decoding**. For each stage, we provide a detailed examination from the viewpoint of LLMs and multimodal LLMs (MLLMs), highlighting their mechanisms and limitations. Furthermore, we identify key challenges and future directions, paving the way for more inclusive and effective TF alignment techniques. By synthesizing and organizing the rapidly growing body of research, this survey offers a guidance for practitioners and advances the development of safer and more reliable LLMs.

## introduction the advent of large language models (llms) @xcite @xcite and multimodal large language models (mllms) @xcite @xcite has marked a paradigm shift in human-machine interaction, enabling tasks ranging from complex reasoning to cross-modal understanding. however, as these models permeate critical domains such as healthcare, education, and public * corresponding authors. discourse, their widespread adoption has ignited a dual-edged societal response: urgent concerns over risks and growing demands for enhanced capabilities. on one hand, llms bring about various risks like generating harmful content @xcite , perpetuating biases and compromising privacy @xcite @xcite . on the other hand, users and industries increasingly demand models that adapt to dynamic knowledge @xcite @xcite , excel in specialized tasks @xcite , and deliver personalized services @xcite . these dual imperatives, namely mitigating risks and fulfilling functional demands, underscore the necessity of aligning llms with human values, ethical standards, and practical requirements. alignment is no longer a technique alone but a prerequisite for responsible and effective deployment. as a critical problem in artificial intelligence and natural language processing, llm alignment has attracted considerable research attention @xcite @xcite . traditional alignment methods primarily rely on fine-tuning (ft), where model parameters are adjusted using curated datasets. while effective in specific scenarios, these approaches face one or more of the following three critical limitations: (1) severe knowledge degradation: ft may overwrite pretrained knowledge, leading to catastrophic forgetting of general capabilities. (2) resource intensity: collecting high-quality alignment data and retraining llms requires prohibitive computational and human resources. (3) access constraint: proprietary models (e.g., gpt-4, gemini) restrict parameter access, rendering ft infeasible. to address these challenges, training-free (tf) alignment has emerged as a versatile paradigm. instead of modifying model parameters, tf meth-dynamic search strategy rain @xcite , deal @xcite , pas @xcite , treebon @xcite , darwin @xcite post-decoding tf alignment ( §3.3) filtering/correcting outputs llm self defense @xcite , ra-llm @xcite , aligner @xcite , eta @xcite , ecso @xcite , privqa @xcite , dps @xcite figure 1 : taxonomy of training-free (tf) alignment methodologies for llms, categorized into pre-decoding, in-decoding, and post-decoding strategies. while tf alignment demonstrates notable advantages, our analysis reveals critical challenges and promising further directions, including (1) maintaining general capabilities, (2) exploring costeffective frameworks, (3) overcoming generalization of tf alignment, (4) developing controllable tf alignment methods. this survey not only seeks to inspire further research interests in this area but also aims to guide the evolution of llms toward benefiting human society. in summary, the main contributions of this paper are as follows: ## preliminary this section systematically explores four pivotal dimensions of tf alignment, including alignmentrelated concepts, the rationale for choosing tf alignment, scenarios where tf alignment is preferable, and methods to evaluate tf alignment approaches. ## tf alignment methods tf alignment methods can intervene at different stages of the generation pipeline without heavily retraining llms. hence we categorize them into pre-decoding, in-decoding, and post-decoding tf alignment, analyzing their application to unimodal llms and mllms. figure 2 shows tf alignment methods at different stages, as well as their specific mechanisms. ## discussion while the above tf alignment methods demonstrate notable advantages, significant challenges still exist. meanwhile, there are many opportunities to make tf alignment more effective, efficient, and adaptive. ## conclusion in this paper, we present a comprehensive survey of training-free (tf) alignment methods for llms, making three significant contributions to the field. first, we establish a conceptual framework for the tf alignment. second, we propose a novel taxonomy that categorizes tf alignment techniques into three distinct paradigms: pre-decoding interventions, in-decoding adjustments, and postdecoding refinement strategies. finally, we identify promising research directions, emphasizing the need for effective, efficient, and adaptive tf alignment methods. this survey is expected to serve both as an introductory guide for newcomers and a reference manual for practitioners, aiming to accelerate progress in developing efficient, ethical, and human-aligned llm systems. we hope that our investigation will inspire novel research addressing the critical challenges and future directions of tf alignment.