ARGS: Alignment as Reward-Guided Search (2402.01694v1)

Abstract: Aligning LLMs with human objectives is paramount, yet common approaches including RLHF suffer from unstable and resource-intensive training. In response to this challenge, we introduce ARGS, Alignment as Reward-Guided Search, a novel framework that integrates alignment into the decoding process, eliminating the need for expensive RL training. By adjusting the model's probabilistic predictions using a reward signal, ARGS generates texts with semantic diversity while being aligned with human preferences, offering a promising and flexible solution for aligning LLMs. Notably, ARGS demonstrates consistent enhancements in average reward compared to baselines across diverse alignment tasks and various model dimensions. For example, under the same greedy-based decoding strategy, our method improves the average reward by 19.56% relative to the baseline and secures a preference or tie score of 64.33% in GPT-4 evaluation. We believe that our framework, emphasizing decoding-time alignment, paves the way for more responsive LLMs in the future. Code is publicly available at: \url{https://github.com/deeplearning-wisc/args}.

• The paper presents a novel decoding-time alignment approach that integrates reward signals to achieve a 19.56% improvement over baseline methods.
• Its methodology modifies LLM predictions with a reward-guided scoring function, bypassing the need for resource-intensive retraining.
• Empirical evaluations on HH-RLHF and Stanford Human Preferences datasets demonstrate improved alignment while preserving lexical diversity.

Insightful Overview of "ARGS: Alignment as Reward-Guided Search"

The paper entitled "ARGS: Alignment as Reward-Guided Search" introduces a novel framework for aligning LLMs with human objectives, a critical challenge in machine learning. Unlike traditional methods such as Reinforcement Learning from Human Feedback (RLHF), which are often resource-intensive and unstable, ARGS simplifies the alignment process by integrating it directly into the decoding phase of text generation. This approach bypasses the need for the expensive retraining associated with RL-based methodologies.

Motivation and Background

LLMs, due to their extensive and varied training datasets, can inadvertently generate inappropriate or misinformed content, necessitating effective alignment strategies. While RLHF has been widely adopted, including in top-tier models like GPT-4, it poses challenges in terms of training stability and adaptability to changing reward models. The introduction of ARGS addresses these issues by allowing for real-time alignment adjustments during text generation, thus providing a flexible alternative to training-based solutions.

Methodology

ARGS operates by modifying the model's probabilistic predictions using a reward signal during the text generation process. Specifically, the framework introduces a reward-guided scoring function that combines the model's predictions with a reward model's feedback. This mechanism allows the model to produce outputs that are semantically coherent and aligned with human preferences. The framework supports both greedy and stochastic token selection strategies, enhancing its applicability across different tasks and model architectures.

Empirical Evaluation

The evaluation of ARGS was conducted on the HH-RLHF dataset, demonstrating notable improvements in model alignment. Specifically, ARGS achieved a 19.56% enhancement in average reward over baseline greedy decoding. The framework also preserved lexical diversity without sacrificing contextual relevance, which suggests that it effectively balances alignment and coherence objectives.

Additionally, the versatility of ARGS was highlighted through its application across various model architectures and alignment tasks, such as those within the Stanford Human Preferences dataset. This adaptability underscores ARGS's potential as a model- and task-agnostic solution for achieving alignment in LLMs.

Comparison and Evaluation

A comparative analysis with existing RL-based methods, like Proximal Policy Optimization (PPO), showed that ARGS not only matches their alignment performance but also significantly excels in generating diverse and contextually integrated outputs. Furthermore, the GPT-4-based evaluations corroborated these findings, with ARGS outperforming other decoding methods in generating qualitatively superior responses.

Implications and Future Work

ARGS introduces a paradigmatic shift from training-phase to decoding-time alignment, which holds substantial promise for the future development of LLMs. By enabling models to adapt quickly to new reward signals and user preferences without retraining, ARGS presents a scalable, efficient, and adaptable approach to model alignment.

In terms of future research directions, exploring more complex tasks and refining reward modeling methods could enhance the utility of ARGS. Moreover, expanding the framework's application to increasingly intricate multi-step reasoning tasks can further validate its efficacy.

In summary, ARGS sets a new direction in the field of AI alignment, providing a robust, efficient, and versatile framework that may inspire further research and application in safer and more aligned AI systems.