Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process (2405.11870v2)

Abstract: Supervised Fine-Tuning (SFT) and Preference Optimization (PO) are two fundamental processes for enhancing the capabilities of LLMs (LMs) post pre-training, aligning them better with human preferences. Although SFT advances in training efficiency, PO delivers better alignment, thus they are often combined. However, common practices simply apply them sequentially without integrating their optimization objectives, ignoring the opportunities to bridge their paradigm gap and take the strengths from both. To obtain a unified understanding, we interpret SFT and PO with two sub-processes -- Preference Estimation and Transition Optimization -- defined at token level within the Markov Decision Process (MDP) framework. This modeling shows that SFT is only a specialized case of PO with inferior estimation and optimization. PO evaluates the quality of model's entire generated answer, whereas SFT only scores predicted tokens based on preceding tokens from target answers. Therefore, SFT overestimates the ability of model, leading to inferior optimization. Building on this view, we introduce Intuitive Fine-Tuning (IFT) to integrate SFT and Preference Optimization into a single process. IFT captures LMs' intuitive sense of the entire answers through a temporal residual connection, but it solely relies on a single policy and the same volume of non-preference-labeled data as SFT. Our experiments show that IFT performs comparably or even superiorly to sequential recipes of SFT and some typical Preference Optimization methods across several tasks, particularly those requires generation, reasoning, and fact-following abilities. An explainable Frozen Lake game further validates the effectiveness of IFT for getting competitive policy.

• The paper introduces IFT, a unified framework that integrates SFT and RLHF through preference estimation and temporal residual connections.
• It demonstrates comparable or superior performance on benchmarks like UltraChat, UltraFeedback, and controlled Frozen Lake scenarios.
• The approach minimizes reliance on extensive human preference labeling, enabling a cost-effective, scalable strategy for fine-tuning LLMs.

Intuitive Fine-Tuning: Towards Unifying SFT and RLHF into a Single Process

The paper at hand proposes an innovative approach to fine-tuning LLMs by unifying Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) under a single process termed as Intuitive Fine-Tuning (IFT). This research, driven by insights into the limitations of current fine-tuning techniques, seeks to optimize LLM alignment with human preferences more efficiently while mitigating computational costs.

Overview and Approach

The authors initially identify a fundamental trade-off when using SFT and RLHF sequentially: while SFT enhances training efficiency, RLHF tends to provide superior alignment with human preferences. Standard practices fail to unify the optimization targets, leading to inefficiencies and compromises in model performance. To overcome this, the paper proposes a new framework that interprets both SFT and RLHF using Preference Estimation and Transition Optimization, defined within a Markov Decision Process (MDP) context. This allows for a more principled integration of RLHF's strengths with the expedience of SFT.

IFT introduces a novel mechanism that leverages a temporal residual connection to capture a model's intuitive assessment of entire answer sequences. Unlike traditional methods that require extensive preference-labeled datasets and complex reward modeling mechanisms, IFT optimizes using a single policy model without the need for auxiliary reference models. The new approach achieves alignment by relying solely on positive samples, similar volumes of data as SFT, thereby enjoying high efficiency.

Empirical Results

The experimentation confirms the efficacy of IFT, demonstrating performance that is comparable or superior to sequential applications of SFT and prominent RLHF alignment methods, notably in tasks requiring generation, reasoning, and fact-following capabilities. The findings hold consistently across several benchmarks, including widely recognized evaluations such as UltraChat and UltraFeedback datasets. The evaluation metrics are further substantiated by testing in the Frozen Lake game scenarios, which serve as simplified, controlled settings to visualize and validate policy improvements.

Practical and Theoretical Implications

Practically, IFT's approach offers a unified alignment procedure that maintains the simplicity and relative low cost of SFT while reaching the alignment quality typical of more resource-intensive RLHF methods. By reducing the reliance on expensive preference labeling and auxiliary data-driven procedures, IFT presents a viable path toward more sustainable and scalable LLM fine-tuning strategies.

Theoretically, this research underscores the importance of viewing SFT and RLHF through a unified lens within an MDP, highlighting opportunities to merge their advantages without incurring substantial downsides. The conceptual framework provided could significantly streamline future advancements in LLM training methodologies, encouraging the formulation of algorithms that inherently integrate diverse learning paradigms.

Future Directions

Future research could extend IFT's framework to explore its scalability across larger models and more diverse linguistic tasks. Observing its performance in real-world applications could offer additional insights into its strengths and limitations. There is also potential in adjusting the parameters of the temporal residual connections to fine-tune the balance between exploration and exploitation more dynamically.

In essence, this paper contributes a novel perspective and methodology for the enhancement of LLMs, providing both a responsible approach to resource utilization and a robust strategy for achieving high-quality human-aligned predictions. This is a noteworthy shift that could potentially shape ongoing efforts and guide future research endeavors in the field of AI-driven language technologies.